Active (Walking and Bicycling, and their variants such as Wheelchairs and Small Wheeled Modes, also called Non-Motorized and Human Powered Transport) play diverse and important roles in an efficient and equitable transportation system:

· In most communities, 10-20% of total trips are made by active modes, making them the second largest mode share, after automobile.

· They provide Affordable Basic Mobility. Non-motorized modes are often critical for trips that society considers particularly valuable, such as access to essential services, education, employment, and social activities by people who are transportation disadvantaged.

· They are resource-efficient travel modes (i.e., they consume minimal road and parking space, impose minimal costs on consumers and the environment) that support TDM objective.

· They are a primary component of Universal Design (transportation systems that accommodate people with disabilities and other special needs).

· They provide Transportation Choice and consumer savings.

· They provide Healthy Exercise and enjoyment.

· They help create more Livable Communities.

· They provide access to Public Transit and so are critical to efforts to make transit more practical and popular.

· They allow and encourage more efficient development (Smart Growth, New Urbanism, Location Efficient Development and Transit Oriented Development).

Homo sapiens are walking animals. Environments that are conducive to walking are conducive to people. Walking is a fundamental and critical activity for physical and mental health. It provides physical exercise and relaxation. It is a social and recreational activity. Walking is also a critical component of the transportation system, providing connections between homes and transit, parking lots and destinations, and within airports. Often, the best way to improve another form of transportation is to facilitate walking.

Walkability improvements provide economic savings and benefits, which are reflected in higher property values in more walkable communities. Cortright (2009), evaluated the effects of walkability on housing prices using the used Walkscore (www.walkscore.com) and 95,000 real estate transactions, controlling for house (size, number of bedrooms and baths, age) and neighborhood characteristics (proximity to the CBD, income, and accessibility to jobs). He found that walkability had a statistically significant, positive impact on housing values. In a typical metropolitan area, each walkscore point increase was associated with a $700 to $3000 increase in home values, after controlling for other observable factors, so for example, that all else held constant, shifting from a 50th to a 75th percentile walkscore increases a house’s value between $4,000 and about $34,000, depending on the market. The researchers conclude that these results reflect the value consumers attach to walkable neighborhoods, which tend to be denser, mixed use neighborhoods with good accessibility, including transit service.

However, non-motorized travel is often overlooked and undervalued. Many conventional travel surveys indicate that only a few percent of total travel is by non-motorized modes, which implies that it is unimportant, and improving non-motorized conditions can do little to solve transport problems. But such surveys tend to undercount non-motorized travel because they ignore short trips, non-work travel, travel by children, recreational travel, and non-motorized links. Actual non-motorized travel is usually three to six times greater than these surveys indicate (Brog, Erl and James 2003; Litman 2003 and 2010). For example, U.S. Census commute data indicates that only 2.8% of commuters walk and only about 0.5% bicycle, since only trips entirely by these modes are counted (bike-bus and drive-walk trips are not included). The National Survey of Bicyclist and Pedestrian Attitudes and Behavior (Gallup 2008) indicates that about half of walking and cycling trips are purely recreational and about half are for transport, and only about 5% are for commuting, so for each non-motorized commute trip there are probably about nine other utilitarian non-motorized trips, and about ten recreational trips.

Conventional transport planning assumes that society is better off if somebody spends 5 minutes driving for an errand than 10 minutes walking or cycling, since it applies an equal or greater cost value to non-motorized trips than motorized trips, only considers vehicle operating costs (vehicle ownership costs, and external impacts such as congestion and parking costs are ignored), and no value is assigned to the health and enjoyment benefits of non-motorized travel. Such assumptions tend to skew countless planning decisions toward motorized travel at the expense of non-motorized travel. For example, it justifies expanding roadways to increase vehicle traffic capacity and speeds, requiring generous amounts of parking at destinations, and locating public facilities along busy suburban roadways, in order to facilitate automobile transportation although each of these tends to reduce walking accessibility.

Non-motorized travel tends to be stigmatized. Some people consider walking and cycling outdated, unsophisticated and unexciting compared with motorized modes, or even as symbols of poverty and failure.

Basic Data and Performance Indicators

Certain types of data are useful for evaluating non-motorized transportation trends and activities (ABW 2010). Performance indicators are data collected specifically to measure progress toward objectives. It is useful to establish standard non-motorized data collection procedures to allow comparisons between different locations and times. The table below lists some types of data that are useful for non-motorized transport evaluation. Some of this data may already be collected, others will require new data collection activities. Note, however, that conventional travel surveys often undercount non-motorized travel, particularly walking, because they ignore short trips, and walking links of motorized trips, as described later in this chapter, so improved travel survey methods may be needed. “Disaggregation” describes how this data should be classified.

Table 1 Non-motorized Transport Data (based on ABC 2000)

Data Type	Disaggregation
Activity
Percentage of total trips by walking and cycling.	User demographics, trip purpose and geographic area.
Average length of walking and cycling trips.	User demographics, trip purpose and geographic area.
Portion of population that walks or cycles on an average day.	User type, trip purpose and geographic area.
Facilities and Conditions
Length (miles or kilometers) of walking facilities (sidewalks and paths).	Type of facility, quality, geographic area.
Portion of streets and roads with walking facilities.	Type of facility, quality, geographic area.
Length (miles or kilometers) of cycling facilities (bike lanes and paths).	Type of facility, quality, geographic area.
Portion of streets and roads with cycling facilities.	Type of facility, quality, geographic area.
Percentage of bicycle network that is continuous.	Type of facility, quality, geographic area.
Quality of cycling conditions on road network.	Type of facility, quality, geographic area.
Bicycle parking and changing facilities at major destinations.	Type of facility, quality, geographic area.
Equipment
Number of bicycles owned per capita.	Type of bicycles, demographics of owners.
Number of bicycles sold annually.	Type of bicycles, demographics of owners.
Number of bicycles stolen per capita.	Type of bicycles, type of theft.
Safety
Number of police-reported walking and cycling crashes, number of hospital-treated walking and cycling injuries, and number of walking and cycling crash fatalities per capita per year.	Victim demographics, type and geographic location of crash.
Portion of cyclists wearing helmets.	Cyclist demographics, type of facility, riding conditions.
Number of bicycle training program graduates.	Type of training program, demographics of participants.
Portion of children who have participated in a bicycle training program when graduating school.	Type of training program, demographics of participants.
Planning and Promotion
Number of non-motorized planning programs.	Type of program.
Funding of non-motorized planning programs.	Type of program.
Number of specialized non-motorized planning staff.	Type of program.
Number and quality of non-motorized encouragement programs.	Participant demographics, activity type and geographic location.
Portion of recreation, mobility management, health, safety and sport programs that include non-motorized encouragement activities.	Participant demographics, activity type and geographic location.

This table lists various types of data that can be collected, as much as possible, for non-motorized planning and evaluation. Some of this data (such as quality of cycling conditions) are discussed later in this chapter.

Measuring Non-motorized Travel In Conventional Travel Surveys

How transport is measured can have a significant effect on transportation planning decisions (Measuring Transportation). Current practices tend to undercount shorter trips, non-work trips, off-peak trips, non-motorized links of motorized trips, travel by children, and recreational travel. As a result, there are usually far more non-motorized trips than what conventional travel Models recognize. About half of walking and cycling trips are purely recreational, about half are utilitarian, and only about 5% are for commuting, so for each non-motorized commute trip there are probably about nine other utilitarian non-motorized trips, and about ten recreational trips.

Conventional transport surveys and models often only count the “primary” mode used between Transportation Analysis Zones (TAZs). Some only count peak-period travel or commute trips. Non-motorized trips to access motorized modes are often ignored in transportation surveys, even if they involve travel on public paths and roads. For example, if a traveler takes 10 minutes to walk to a bus stop, rides on the bus for five minutes, and takes another five minute walk to their destination, this walk-transit-walk trip is usually coded simply as a transit trip for analysis, even though the non-motorized links take more time than the motorized link. Walking trips from a parking space to a destination, or between nearby buildings, are often ignored. Similarly, many types of pedestrian activities are ignored in conventional field surveys, such as people sitting or waiting on sidewalks, skaters and skateboarders, and people walking from cars or buses to buildings (Haze 2000).

The Bicycling and Walking Benchmarking Reports (ABW 2010 and 2012) summarizes current walking and cycling activity in the U.S., by city and state, and provides data from some other countries based on various sources. Weinstein and Schimek (2005) discuss problems obtaining reliable non-motorized information in conventional travel surveys, and summarize walking data in the U.S. 2001 National Household Travel Survey (NHTS). They find that about 10% of total measured trips involved non-motorized travel (about 16% of these walking trips were to access public transit), making walking the second most common mode after automobile travel. Overall, people average 3.8 weekly walking trips, but some people walk much more than others. About 15% of respondents report walking on a particular day, and about 65% of respondents reported walking during the previous week. The median walk trip took 10 minutes and was about ¼ mile in length, much less than the mean walking trip (i.e., a small number of walking trips are much longer in time and distance). The table below summarizes walking trip data.

Table 2 NHTS Walking Trip Attributes (Weinstein and Schimek 2005)

Purpose	Frequency	Mean Distance	Median Distance	Mean Duration
	Percent	Mile	Mile	Minutes
Personal business/shopping/errands	48%	0.44	0.22	11.9
Recreation/exercise	20%	1.16	0.56	25.3
To transit	16%	N/A	N/A	19.6
To or from school	7%	0.62	0.33	13.3
To or from work	4%	0.78	0.25	14.1
Walk dog	3%	0.71	0.25	19.0
Other	2%	0.57	0.22	14.8
Totals	100%	0.68	0.25	16.4

This table summarizes the results of NPTS walking trip data. N/A = not available.

They reach the following overall conclusions about U.S. walking activity:

· Most Americans walk very little. The vast majority (84%), reported no walk trips in their daily diaries. More than one-third reported no walk trips within the previous week.

· Exercise and recreational trips account for more than one quarter of walk trips, a significant share. Because these average twice the distance of other walk trips, they account for about half the share of total distance walked. However, the determinants of exercise trips are completely different than those of utility walk trips.

· Transit access trips are a significant component of total walking, comprising 16% of all walk trips. This finding suggests that improving the pedestrian environment might be an important component of making transit more attractive, and that increased transit use could significantly increase daily physical activity.

· The mean trip distance for utility walk trips, 0.5 miles, was remarkably constant across many demographic groups and in different neighborhood densities. This may suggest that it is reasonable for planning purposes to take this figure of half a mile as a maximum that many Americans would be willing to walk in ordinary circumstances.

· For those who do walk, walking can make a significant contribution to the Surgeon General’s suggestion of at least 30 minutes of daily exercise. Respondents who walked to transit averaged 26 minutes of walking per day (2.0 trips x 12.8 minutes/trip), those who walked or jogged for recreation averaged 41 minutes per day (1.6 trips x 25.7 minutes/trip), and those who walked for other purposes averaged 28 minutes per day (2.2 x 12.5 minutes/trip).

· Increased land use density by itself has only a modest impact on walking activity compared with other factors.

The UK National Travel Survey (NTS) and the US National Household Travel Survey (NHTS) attempt to collect more comprehensive data on non-motorized travel. The NHTS indicate that about 12% of total trips are by non-motorized modes. Walking and cycling represent a relatively large portion of shorter trips, as illustrated in Table 3. More than half of trips of a mile or less, and nearly a third of trips of three miles or less, are by walking or bicycling.

Table 3 Shorter Trip Mode Share (Litman 2010, based on NHTS 2009)

Trip Distance	Portion of Total Trips	Walk	Bike	Transit	POV	Totals
0.5 or less	10%	61%	3.1%	1.5%	34%	100%
1.0 or less	19%	51%	3.3%	3.0%	42%	100%
3.0 or less	41%	27%	2.2%	3.9%	67%	100%

This table summarizes mode share of shorter trip distance categories.

Many of these shorter trips are links in longer trips, including a series of automobile trips to multiple destinations when running errands, walking to and from transit stops, and walking a few blocks to and from a parked car.

Nordback, Sellinger and Phillips (2017) evaluate three general methods for evaluating cycling and walking activity. The first approach employs travel survey data. The second approach is sample-based using pedestrian and bicycle count data. The third approach is an aggregate demand model approach using demographic data combined with count data. Each method has strengths and weaknesses, and each helps us understand bicycle and pedestrian travel in different ways. Due to data limitations, none of these methods could be properly implemented on the statewide level. The travel survey method estimated the lowest bicycle and pedestrian miles traveled (BMT and PMT), and the sample-based method estimated the highest. The travel survey method is useful for a statewide measure, but does not provide the detail needed for facility-level estimates. The project team recommends improving both statewide travel survey data and pedestrian and cyclist traffic count data which feed these methods. Travel survey data should be collected statewide with oversampling for non-motorized travelers. Pedestrian and cyclist traffic counts should be expanded to include a continuous counting program in addition to the short-duration count program. After the continuous count program is in place, short-duration counts should be chosen using a stratified random sampling approach.

Rietveld (2000) finds that the actual number of non-motorized trips is six times greater than what conventional surveys indicate. Similarly, in Germany only 22% of trips are completely by walking, but 70% include some walking (Brog, Erl and James 2003). The Southern California Metropolitan Transportation Authority increased the portion of non-motorized travel in their models from about 2% of regional trips (based on conventional travel surveys) up to about 10% in response to the more comprehensive travel data provided in the 2001 National Household Travel Survey (www.bts.gov/nhts), which obtained more detailed information on walking trips than most travel surveys. It found that walking represents 8.6% of total personal trips, about 50% more than reported in the 1995 National Personal Travel Survey, which used more conventional survey methods. Chu (2003) used NPTS data to calculate average minutes walked by various demographic groups.

Besser and Dannenberg (2005) used the 2001 National Household Travel Survey to analyze the amount of walking associated with public transit trips, and factors that affect this activity. They found that Americans who use public transit on a particular day spend a median of 19 daily minutes walking to and from transit, and that 29% achieve the recommended 30 minutes of physical activity a day solely by walking to and from transit. In multivariate analysis, rail transit, lower-income, age, minority status, being female, being a nondrivers or zero-vehicle household, and population density were all positively associated with the amount of time spent walking to transit.

Winters, et al (2007) used data from the 2003 Canadian Community Health Survey (CCHS) and various other statistics to evaluate factors affecting utilitarian cycling rates. They found that the proportion of the urban population reporting bicycling in a typical week was 7.9%, with students cycling more than nonstudents (17.2% vs 6.0%). In the general population, older age, female gender, lower education, and higher income were associated with lower likelihood of cycling. More days of precipitation per year and more days of freezing temperatures per year were both associated with lower levels of utilitarian cycling, although there was less variation in the proportion of students who cycled by age and income. Albey and Turner (2011) develop a model for calculating the number of walking trips that occur on a street based on sidewalk and crosswalk conditions.

Travel intercept surveys ask people how they travel to a particular destination. For example, a survey of shoppers in Seattle neighborhood business districts found that only 3-20% of local residents drive to local shops (SDOT 2011). Most residents (61%+), walk, bicycle or take transit.

To their credit, many transportation professionals give non-motorized transportation more consideration than what is implied by the available travel survey data. They realize that non-motorized travel has many critical functions in an efficient and balanced transportation system, some of which are difficult to measure. However, this occurs in spite of, rather than supported by, conventional transportation data analysis. There is much that can be done to improve transportation planning and Modeling to better evaluate non-motorized transportation.

Information on current walking and cycling travel can be gathered in the following ways (Leather, et al. 2011):

1. Travel surveys can be designed to elicit sufficient responses concerning non-motorized travel. For example, “travel” should be clearly defined to include walking and bicycling trips. Short, non-work and recreational trips, and trips by children should be counted.

2. A special survey targeting cyclists and pedestrians (such as survey forms distributed through bicycle shops, sport clubs, recreation centers, colleges, and schools). Surveys can be handed out to cyclists and pedestrians as they travel along a street or path. Surveys should include special user groups, such as people in wheelchairs and elderly pedestrians, particularly in areas they frequent.

3. Traffic counts that gather information on pedestrian and bicycle travel. These can include photoelectric counters installed on trails, electronic counters installed on cycle paths and bike lanes, and manual counts. Volunteers from pedestrian and cycling organizations may also be mobilized to perform manual counts for non-motorized travel.

Surveys should gather the following information on non-motorized travel:

· Who – Demographic information such as age, gender, residence location, employment status, and income.

· Where – Origin and destination of trips, including links by other modes (such as transit).

· When – Time, day of the week, day of the year, and conditions, such as weather, road conditions, and traffic conditions.

· Why – Purpose of trip. What factors affected travel choice (for example, would a cyclist have chosen another route or mode if road conditions or facilities were different).

Measuring Non-motorized Travel Demand

Non-motorized travel demand refers to how much people would use non-motorized modes under various circumstances. A number of specific factors can affect demand for non-motorized transport in a particular situation (Charlier Associates, Krizek and Forsyth 2012; Clifton, et al. 2015; Schneider, Patten and Toole 2005; McDonald, et al. 2007; Krizek, et al. 2007; Pike 2011; Leather et al. 2011; Vale, Saraiva and Pereira 2016). These include:

· Attractions. Certain activity centers tend to be major attractors for walking and cycling, including commercial districts, school-college-university campuses, employment centers, recreation centers and parks.

· Trip distance. Most walking trips are less than a mile, and most bicycling trips less than 5 miles in length, although recreational trips are often much longer (Iacono, Krizek and El-Geneidy, 2008).

· Demographics. Young (10-20 years), elderly, and low-income people tend to rely more on walking for transport. Young and low-income people tend to rely on cycling for transport. Households with lower vehicle ownership rates tend to rely more on non-motorized modes than those with one vehicle per driver.

· Land use patterns (density and mix). Walking and bicycling for transportation tend to increase with density (i.e., number of residents and businesses in a given area) because higher density makes these modes more efficient.

· Travel conditions. Wide roads with heavy, high-speed vehicle traffic can form significant barriers to non-motorized travel. Special facilities for non-motorized travel (sidewalks, wide curb lanes, and paths), their condition and connectivity can have a significant impact on the amount of walking and bicycling that occurs.

· Topography and climate. These factors can affect walking and bicycling, but not as much as might be expected. For example, the cities of Seattle, Portland and Missoula report significantly higher levels of cycle transportation than many “Sunbelt” cities that are flat and have mild climates.

· Community attitudes. Local attitudes can have a major impact on the level of cycling in a community. For example, it may be unremarkable that cycling tends to be high among college students and staff, but many college towns find that cycling is also relatively common among people who have not formal affiliation with the college simply because it has become an acceptable form of transportation. This indicates that some people hesitate to cycle, but will if they perceive it to be more socially acceptable.

· Time and geographic scope. It may take several years for a community to fully achieve its full non-motorized travel potential. First year impacts are frequently modest, but tend to increase as individuals become more accustomed to non-motorized travel and as additional support facilities (pedestrian and bicycle network, bicycle parking, etc.) develop.

Using available travel surveys Barnes and Krizek (2005a) estimate that on average roughly 1% of adults in the United States ride a bicycle during a particular day. Over large geographic areas such as metropolitan areas or states, this number ranges roughly between about 0.3% and 2.5%. Over smaller areas such as specific parts of metropolitan areas, the range could go as high as 15%. They concluding that total cycling can be estimated in a particular area as 0.3% plus 1.5 times the commute share.

In a detailed baseline survey of adults residents in five U.S. communities performed before major non-motorized improvements were implemented, Krizek, et al. (2007) found that on a given day, 15% to 35% of adults in the surveyed communities walked for transportation, while 2% to 4% bicycled. The average daily walking distance for those who did walk for transport is 1.5 to 2 miles, while cyclists rode an average of 5 to 8 miles. About 30% to 40% of bicycle or walking commute trips, and about 95% of non-motorized trips to other destinations, would otherwise have been made by driving. They estimate that walking and cycling reduce approximately 0.25 to 0.75 mile of daily driving per adult resident, or 1-4% of total automobile travel. Some of this motorized travel would be ridesharing, in which passengers use an otherwise empty seat in a vehicle that would make the trip anyway, but others generate additional vehicle travel, including some chauffeured trip in which a driver makes a special trip to carry a passenger, which often generates an empty return trip.

Phillips, Karachepone and Landis (2001) describe various ways to estimate demand for motorized transportation. Transportation surveys such as the National Personal Transportation Survey (Transportation Statistics) provide information on non-motorized travel. Rossi (2000) describes technical information on non-motorized demand models developed in the Boston, Portland and Philadelphia regions. University of North Carolina (1994) and Clarke and Tracy (1995) summarize data from various studies on non-motorized travel demand, including survey data from various communities, and discussion of factors that affect walking and cycling activity. Desyllas, et al. (2003) used Multiple Regression Analysis to model pedestrian travel demand in the city of London, taking into account take walkway conditions, nearby land uses (trip generators), street network connectivity and transport accessibility (proximity to Tube stations and other transport terminals). McDonald, et al. (2007) develop a model for predicting non-motorized travel demand and the impacts that on- and off-road walking and cycling facilities will have on the use of these modes.

Schneider, Patten and Toole (2005) describe non-motorized travel surveys used in various communities, including manual counts, automated counts, surveys targeting non-motorized users, surveys sampling a general population, inventories, and spatial analyses. Cao, Handy and Mokhtarian (2006) used a travel survey performed in Austin, Texas to evaluate the effects of land use patterns on strolling trips (walking for pleasure or exercise) and utilitarian walking trips. The found that the pedestrian environment at the origin (home) has the greatest impact on strolling trips, while the pedestrian environment at the destination appears to be at least as important for utilitarian trips. They also found that people are more likely to stroll around or walk to the store when fewer vehicles travel residential and commercial streets. They found that strolling accounts for the majority of walking trips, and tends to be undercounted.

Petritsch, et al. (2008a and 2008b) develop models for predicting the increases in cycling activity, reductions in motorized travel, and resulting health and energy conservation benefits likely to result from cycling facility improvements. They find that a cycling network’s overall quality has a greater influence on the volume of cyclists in an area than any specific facility, indicating significant network effects. In a detailed literature review, Pucher, Dill and Handy (2010) conclude that improving cycling infrastructure (paths and lanes, Bike Parking, Bike on Transit support) do tend to increase cycling activity, although the impacts are often small and vary depending on specific types of improvements and additional support factors.

Pucher and Buehler (2006) find that despite a colder climate, Canadians cycle about three times more than Americans. Reasons for this difference include Canada’s higher urban densities and mixed-use development, shorter trip distances, lower incomes, higher costs of owning, driving and parking a car, safer cycling conditions, and more extensive cycling infrastructure and training programs. The researchers point out that most of these factors result from differences in transport and land-use policies, and not from intrinsic differences in history, culture or resource availability. They suggest that it is possible to significantly increase cycling levels in the United States by adopting Canadian policies that have promoted cycling and enhanced its safety.

Pedestrian Location Identifier

Moudon (2001) developed Pedestrian Location Identifier methodologies (PLIs), which use GIS and remote sensing tools to identify suburban, postwar areas that have land use characteristics likely to create substantial latent demand for pedestrian travel. Two tools were developed that use different sets of databases. The Pedestrian Location Identifier One, relies on readily available census data, GIS software, and aerial photographs. It is a manual method that requires an individual analyst’s judgment when comparing the data from the census to aerial photographs to delineate clusters. The Pedestrian Location Identifier Two uses parcel-level data with GIS software, which requires information that only MPOs, large cities, and some counties and states may have. It is an automated method, which uses the GIS software to define and analyze pedestrian locations.

National Survey of Pedestrians and Bicyclists Attitudes and Behaviors (www.walkinginfo.org/pdf/bikesurvey.pdf)

The 2002 National Survey of Pedestrians and Bicyclists Attitudes and Behaviors, which involved phone interviews with more than 9,600 adults age 16 and older throughout the U.S., found the following:

· Nearly 80% of adult Americans take at least one walk of five minutes or longer during the summer months, while fewer than 30% ride a bike, according to a major new survey released today by the U.S. Department of Transportation.

· Bicycling is most common among younger residents. Nearly 40% aged 16 to 24 ride a bicycle during the summer, 26% aged 45 to 54 cycle, but only about 9% of those age 65 and older report they cycle.

· Walking declines slightly as people age. Eighty-two percent of those aged 16 to 24 take walks, 80% aged 45 to 54 walk, and 65% aged 65 and older report taking walks.

· Only half of all adults are satisfied with their communities’ designs for bicycling safety, whereas three out of four adults are satisfied with their communities’ designs for pedestrian safety.

· Survey respondents were also asked to recommend changes to their communities for either bicycling or walking. Most persons suggested changes in bicycle and pedestrian facilities. For those recommending changes, 73% wanted new bicycle facilities, such as trails, bicycle lanes and traffic signal, and 74% wanted pedestrian facilities including sidewalks, lighting and crosswalks.

· People who do not take walks cite these reasons: disability or other health problems (25%); unfavorable weather (22%); and too busy or no opportunity (19%). Those who do not bike cite lack of access to a bicycle (26%); too busy or no opportunity (17%); disability or other health problems (10%).

· Males are more likely to take a bike ride during the summer than are females. However, both groups are about equally likely to take walks during the summer.

Mode Shifts

The benefits of a non-motorized program are affected by their travel impacts, including increases in non-motorized travel and reductions in motorized vehicle travel. Shifts from automobile to non-motorized modes are measured by mode substitution rates, that is, the ratio between increased non-motorized person-miles and reduced motor vehicle-miles.

When automobile travel is reduced in response to disincentives such as increased vehicle fees or vehicle restrictions, a significant portion (typically 10% to 50%) of reduced trips shift to non-motorized modes (Transport Elasticities). Shorter trips (less than three miles) shift to non-motorized modes, and longer trips shift to combined transit and non-motorized trips. For example, when UK residents were asked how they could reduce short (less than 8 kms) vehicle trips, respondents indicated they could shift 31% of these trips to bus, 31% to walking, and 7% to bicycle (Mackett 2001). After Canadian fuel prices increased about 15% in 2001, a federal Competition Bureau survey found that about a quarter of motorists shifted some automobile travel to other modes, of which 46% took transit, 36% walked, 24% cycled, and 20% shared car rides. Parking Cash Out (allowing commuters to exchange a free parking space for cash) caused a 13-point reduction in automobile trips, a 9-point increase in carpooling, a 9-point increase in transit use, and a 1-point increase in non-motorized commute trips.

Table 4 Average Auto Trips Displaced by Non-motorized Modes Trip Purpose (From The National Bicycling and Walking Study, FHWA 1993)

Mode	Commute/Personal	Commercial	Recreation	Children	Average
Bicycle	62.5%	62.5%	50.0%	29.0%	38.0%
Pedestrian	50%	33.0%	21.0%	19.0%	26.0%

When non-motorized travel increases due to improved conditions, not all new walking and cycling trips substitute for automobile trips, some reflect increased total travel (including recreational trips) or shifts from transit or ridesharing. Typically, 20% to 60% of increased non-motorized travel substitutes for motorized travel, depending on conditions.

In addition to person-miles shifted from motorized to non-motorized travel, increased non-motorized transportation tends to leverage additional vehicle travel reductions. A short walking or cycling trip often replaces a longer automobile trip, for example, people may choose between walking to a nearby store or driving to a more distant shopping center. Pedestrians and cyclists often use shortcuts unavailable to drivers. When people shift to non-motorized travel for a particular trip, or when households reduce their vehicle ownership due to improved non-motorized conditions, they tend to reduce their total vehicle mileage by avoiding discretionary trips. Non-motorized transport supports Smart Growth land use patterns (more compact, mixed, multi-modal development) that reduce travel distances and total motorized travel.

Various land use factors can affect non-motorized travel activity, including density, mix and non-motorized facilities (Marcus 2008). Figure 1 shows average per capita annual vehicle mileage in U.S. cities categorized by non-motorized commute mode split. As non-motorized travel increases, vehicle mileage declines. Although non-motorized mode split is small (representing less than 5% of trips and probably less than 1% of person-miles, since non-motorized trips average less than a fifth the distance of motorized trips), the mileage differences are large. Each percentage point increase in non-motorized transport is associated with about 700 fewer annual vehicle-miles. Assuming commute mode split is representative of total personal travel, urban residents average 10,000 annual person-miles, and non-motorized trips average one mile in length, each non-motorized mile is associated with seven reduced vehicle-miles.

Figure 1 U.S. Non-motorized Vs. Motorized Transport (Census and FHWA Data, 2000)

Per capita vehicle mileage tends to decline as non-motorized travel mode split increases.

International data also indicate that increased non-motorized transport is associated with reduced driving, as indicated in Figure 2. Of course, association does not prove causation. Not every walking or cycling trip causes seven miles of reduced driving. The lower vehicle mileage in cities with relatively high non-motorized mode split reflects various land use and transport system factors, such as density, mix, street design, parking supply, and pricing which affect the relative attractiveness of motorized and non-motorized travel. But programs that increase non-motorized travel tend to create such communities, which is to say that smart growth supports non-motorized travel and non-motorized travel supports smart growth. As a result, mobility management programs that increase non-motorized transport usually leverage reduced motorized travel, causing proportionately larger reduction in vehicle-miles, although exactly how much depends on the situation.

Figure 2 Non-motorized Vs. Motorized Transport (Kenworthy and Laube 2000)

International data show that vehicle travel tends to decline as non-motorized travel increases.

These leverage effects probably apply only to non-motorized travel used for transportation purposes, not to recreation walking and cycling. For mobility management evaluation an important question is the degree that factors that can be changed through public policies can increase non-motorized travel and leverage reductions in motorized travel in the short or medium term. If higher non-motorized transport and lower motor vehicle mileage in different geographic areas completely reflect the legacy of patterns established decades earlier, it may be futile to try to change them. However, at least some of factors can be changed by public policies in medium-term, including non-motorized facility quality, traffic management practices, financial incentives (such as road and parking pricing) and public information and attitudes can be changed in the short term, and other factors, such as the location of public facilities, the design of new buildings, and community redevelopment practices. Many communities have experienced significant non-motorized travel growth and reductions in non-motorized travel over a few years due to policy changes and mobility management programs (Success Stories).

Sciara, Handy and Boarnet (2014) and Handy, Tal and Boarnet (2014) summarize research on the effects of pedestrian and bicycling improvements and encouragement programs on travel activity. Some experts conclude that walking and cycling can do little to solve transportation problems because they only consider current commute trips that can shift completely to non-motorized modes. But other studies give more positive assessments of potential travel impacts. According to some studies, 5-10% of urban automobile trips can reasonably be shifted to non-motorized transport (Mackett 2000; Cairns et al. 2004).

Figure 3 Urban Mode Split (Pucher and Lefevre 1996)

This figure shows the portion of urban travel by different modes in various countries. Non-motorized travel varies significantly from one country to another.

For example, the Australian TravelSmart program uses various incentives to encourage residents to use alternative travel modes. Before-and-after surveys find that automobile trips decline by 5% to 14%, and that about half of these reductions result from shifts to non-motorized travel. Rates of non-motorized travel vary significantly from one community to another, depending on land use patterns, transportation system design factors, and community attitudes, as indicated in Figure 3. Even relatively cold and hilly countries, such as Sweden, Switzerland and Germany achieve high levels of non-motorized travel. Similarly, some North American communities have much higher rates of non-motorized travel due to supportive Land Use policies.

Most communities appear to have significant latent demand for pedestrian travel, that is, people would walk more frequently if they had suitable facilities and resources. One US survey found that 38% of respondents would like to walk to work, and 80% would like to walk more for exercise (STPP 2003).

Current Practices

The transportation planning process often uses economic evaluation to select and design transportation projects and programs. In this context, economic refers to any valuable resource, including time, health, land and environmental quality, not just money. During the last century, transportation organizations have developed various economic evaluation methods and tools (DfT 2014; Litman 2009; NZTA 2016; Transportation Economics Committee 2014). Most of these tools were originally developed to evaluate highway projects and so they focus on roadway improvement impacts: travel time, vehicle operation, accident, and pollution emission rates; other impacts tend to be overlooked (Table 1); the overlooked impacts are not entirely ignored but are generally described as intangibles, with the implication that they are difficult to measure and unimportant, and so are excluded for benefit/cost analysis.

Many important walking benefits tend to overlooked. For example, improving walkability can improve mobility options for non-drivers, which reduces chauffeuring burdens and helps achieve social equity objectives (it helps disadvantaged people); increase public fitness and health; and provide vehicle ownership and parking cost savings. Walkability can help create more compact, multi-modal communities, which leverages additional reductions in automobile travel, beyond just the travel that shifts to walking, and so provides additional benefits.

Table 1 Scope of Impacts Generally Considered and Overlooked (Litman 2013)

Generally Considered

Often Overlooked

• Financial costs to governments

• Travel speed (reduced delays)

• Vehicle operating costs (fuel, tire wear, tolls, etc.)

• Per-kilometre crash rates

• Per-kilometre pollution emission rates

• Generated traffic and downstream congestion

• Parking costs

• Vehicle ownership costs and overall affordability

• Indirect environmental impacts

• Strategic development objectives

• Mobility for non-drivers and social equity objectives

• Public fitness and health

Conventional economic evaluation overlooks many impacts, which tends to undervalue walking.

Several recent studies evaluate active transport (walking, cycling and their variants) economic impacts (Cavill, et al. 2008; Rabl and de Nazelle 2012; WHO 2014), but these often focus on cycling and consider a limited set of impacts, for example, health and pollution reduction benefits, but overlook other important impacts (DfT 2008; Gotschi 2011; Krizek, et al. 2006).

Active Transport (AT) Economic Impacts

Table 2 identifies economic impacts (benefits and costs) that result from various types of transportation system changes associated with walking and bicycling. Some result from improved conditions, others from increased active transport activity or reduced automobile travel, and some from more compact development (called New Urbanism or Smart Growth) that result.

Table 2 Walking and Cycling Benefits and Costs

	Improved AT Conditions	Increased AT Activity	Reduced Automobile Travel	More Compact Communities
How Measured	Walkability, using indicators such as pedestrian Level-of-Service (LOS)	Increased distance walked	Reductions in motor vehicle travel	More compact development, and resulting reductions in vehicle ownership and use
Benefits	· Improved user convenience, safety and comfort · Basic mobility for non-drivers, which supports equity objectives · Higher local property values and business activity	· User enjoyment · Improved public fitness and health · Increased community cohesion (positive interactions among neighbors) which tends to increase security	· Reduced traffic and parking congestion · Road and parking facility cost savings · Consumer savings and affordability · Reduced chauffeuring burdens · Reduced crash risk · Energy conservation · Pollution reductions	· Openspace preservation · Improved accessibility, particularly for non-drivers · More efficient public infrastructure and services · Economic development
Costs	· Facility costs · Lower traffic speeds	· Equipment costs (shoes, bikes, etc.) · Increased crash risk	· Slower travel speeds when people shift from motorized modes	· Increases in some development costs

Walkability improvements can have various benefits and costs. It is important to consider all of these impacts when evaluating a particular program or project.

Because many impacts depend on the amount that walking and automobile travel changes, economic evaluation requires quantifying these impacts. Conventional travel demand models, which were developed primarily to predict motor vehicle traffic impacts, their geographic scale and inputs are poorly suited for predicting walking impacts. Better models that provide more accurate walking predictions are becoming available (Abley and Turner 2011; Clifton, et al. 2015; Kuzmyak, et al. 2014), and conventional models can be supplemented with estimates based on appropriate examples and case studies (Pratt, et al. 2010).

There is evidence of significant latent demand for walking. For example, the U.S. Federal Highway Administration sponsored a Nonmotorized Transportation Pilot Program, which invested about $100 per capita in pedestrian and cycling improvements in four typical U.S. communities (Columbia, Missouri; Marin County, California; Minneapolis, Minnesota; and Sheboygan County, Wisconsin), increased walking trips by 23% and cycling trips by 48%, mostly for utilitarian purposes that would otherwise generate motor vehicle trips (FHWA 2014).

Although it can be difficult to predict the impacts of individual walking improvements, there is good evidence that a community’s walkability significantly affects overall travel activity: residents of walkable neighborhoods tend to own fewer vehicles, drive less and rely more on alternative modes than they otherwise would (Guo and Gandavarapu 2010; Sciara, Handy and Boarnet 2014). Research by Frank, et al. (2011) indicates that increasing a neighborhood’s sidewalk coverage ratio from 0.57 (sidewalks on both sides of 30% of all streets) to 1.4 (sidewalks on both sides of 70% of streets) could reduce vehicle travel 3.4% and carbon emissions 4.9%.

Under favorable conditions, walkability improvements and leverage proportionately larger automobile travel reductions, so an additional mile of walking reduces more than one vehicle-mile. For example, Guo and Gandavarapu (2010) used travel survey data from Dane County, Wisconsin to evaluate how various urban design factors (including the existence of sidewalks) affects residents’ travel activity. Their model predicts that installing sidewalks on all streets in a typical North American community would increase per capita walking and cycling by 0.097 average daily miles and reduce automobile travel by 1.142 daily vehicle-miles, about 12 miles of reduced driving for each mile of increased active travel. This occurs because shorter walking trips often substitutes for longer automobile trips, such as walking to a local store rather than driving to one that is more distant, and walking can reduce chauffeuring trips (special vehicle travel needed to transport a non-driver) that often have empty backhauls. Walkability improvements also support public transit travel, and help create more compact, multi-modal communities, which also help reduce per capita vehicle ownership and use.

Defining and Measuring Economic Impacts

This section describes the various categories of economic impacts and how they can be measured.

Impacts of Improved Walkability

These result from improved walking conditions, unrelated to changes in travel activity. This can be measured using indicators such as pedestrian Level-of-Service.

Improved Pedestrian Convenience, Safety and Comfort

Sidewalk, path, crosswalk and streetscaping improvements tend to increase walking convenience, safety and comfort, which benefits both existing and new users. Such improvements can be quantified using Level-of-Service (LOS) ratings, which rate conditions from A (best) to F (worst), similar to school grades (Brozen, et al. 2014; Dowling, et al. 2008).

Direct benefits can be valued using stated preference surveys that ask potential users their willingness to pay for specific walkability improvements, and by adjusting travel time unit costs (dollars per hour) to reflect walking conditions. Transportation agencies use standard methods for valuing travel time and accident risk. For example, pedestrian travel time is generally valued at 50-100% of average wages, with higher cost values for uncomfortable travel conditions, and each avoided traffic death is generally valued at $2-6 million, with lower values for reduced injuries (Blincoe, et al. 2014; DfT 2009; Litman 2009; NZTA 2016). Table 3 illustrates how travel time cost wage multipliers can be adjusted based on pedestrian Level-of-Service (LOS) ratings. For example, if wages average $20 per hour, a project that improves walking conditions from E (84% of wages) to B (50% of wages) can be considered to provide benefits worth $6.80 per hour walked on that facility ([84% - 50%] x $20 = $6.80). A LOS B (50% of wages) short-cut that saves 1,000 annual hours of walking time can be valued at $10,000 annually (1,000 x 50% x $20).

Table 3 Recommended Travel Time Values by Level-of-Service (Litman 2008)

LOS Rating	A-C	D	E	F
Description	Good	Poor	Very Poor	Impassible or Dangerous
Wage Multiplier	50%	67%	84%	100%

Travel time unit costs (dollars per hour) can be adjusted to reflect pedestrian Level-of-Service (LOS).

Basic Mobility

Basic mobility refers to people’s ability to access essential services and activities, including healthcare, shops, school, work and recreational activities. Walkability improvements, particularly universal design features such as curb cuts and ramps, tend to provide basic mobility, which helps achieve social equity objectives by benefiting economically and socially disadvantaged people. This provides direct benefits to the people who gain independent mobility, and option value people who do not currently walk on the improved facility but appreciate having it available for their neighbors who cannot drive, and in case they ever need to use such facilities in the future.

Option value can be measured using surveys of residents, and multiplying this times the number of nearby residents who could use the improved pedestrian facilities (DfT 2014). For example, if a survey indicates that neighborhood residents would willingly devote, on average, $50 annually of their local transportation budget to pedestrian improvements, this could justify $50,000 annual spending in a neighborhood with 1,000 residents. Basic mobility benefits can also be valued based on public transit subsidies. For example, if public transit or school bus services are subsidized at $1.00 per passenger-mile, pedestrian improvements that serve similar travellers and trips can be valued at the same rate.

Pedestrian facility investments are also justified on social equity grounds. In most communities, 20-40% rely on non-automobile travel for basic mobility, due to age, disability, low incomes or preferences. As previously descried, current planning practices are biased in various ways that favor automobile travel over other modes, which is unfair (horizontally inequitable), and since disadvantaged people tend to rely on non-automobile modes, it is also regressive (vertically inequitable). Public investments in walking, cycling and public transit services ensure that non-drivers receive a fair share of public expenditures on transportation.

Property Value and Business Activity

Pedestrian improvements, including sidewalks, paths and streetscaping, often increase nearby property values (Bartholomew and Ewing 2011; Karadeniz 2008; Racca and Dhanju 2006). For example, Cortright (2009) found that in a typical metropolitan area, each one-point increase in the 100-point Walkscore Index is associated with a $700 to $3,000 increase in home values, so for example, shifting from a 50th to a 75th percentile Walk Score rating increases a house’s value $4,000 to $34,000, depending on the market. Karadeniz (2008) found that each foot closer to Ohio’s Little Miami Scenic Trail increases single-family property sale prices $7.05, indicating that values increase 4% if located 1,000 feet closer to the trail.

Pedestrian improvements, particularly streetscaping that creates more attractive commercial streets, often increases local business activity (Fleming, Turner and Tarjomi 2013; Schlossberg, et al. 2013). For example, after downtown Salt Lake City’s South Broadway Avenue was improved with pedestrian crossings, median islands, bike lanes, planters, artwork and colored pavements, local businesses’ gross receipts increased 8.8% (SLCDOT 2015).

Facility Costs

Of course, walkability improvements often have costs. These costs can be estimated based on the report, Costs for Pedestrian and Bicyclist Infrastructure Improvements: A Resource for Researchers, Engineers, Planners, and the General Public (Bushell, et al. 2013) and, Guidelines for Analysis of Investments in Bicycle Facilities (Krizek, et al. 2006). Dutch cities typically spend €10 to €25 annually per capita on cycling facilities, which is considered high but increases cycling activity (Fietsberaad 2008).

Impacts Caused by Increased Walking Activity

These benefits result from increases in walking activity, measured as the miles that people walk or walking mode share.

User Enjoyment

People, and their pets, often enjoy walking. Walking generally ranks as the most common of all outdoor recreational activities, and people often walk for utilitarian trips even if they could drive, for enjoyment sake. As a result, walkability improvements that allow more walking tend to increase total enjoyment.

The enjoyment value of walking can be estimated using surveys which ask potential facility users to estimate their willingness-to-pay for improved walking conditions, and it can be inferred by the amount that walking activity increases after pedestrian conditions improve.

Improved Public Fitness and Health

Health experts recommend that children exercise least 60 daily minutes, adults at least 150 weekly minutes, and older adults at least 300 weekly minutes (CDC 2008). In many communities less than half the population achieves these targets. Although there are many possible ways to exercise, including organized sports and gym workouts, walking is often the most practical and cost effective way to increase public fitness, particularly for higher risk people who are currently sedentary and overweight (Murthy 2015). As a result, walkability improvements tend to provide public fitness and health benefits (Mackett and Brown 2011).

Several studies have estimated the economic value of walking and cycling health benefits (Cavill, Cope and Kennedy 2009). The Health Economic Assessment Tool (HEAT) Tool calculates these benefits in a particular situation (WHO 2014). It indicates that health benefits are typically worth more than $3.00 per mile walked (NZTA 2016). Using a more sophisticated, dynamic model, Mansfield and Gibson (2015) calculate significantly lower but still substantial health benefits, suggesting that an additional mile walked provides $1.00 to $3.00 worth of health benefits.

Community Cohesion and Increased Security

Community cohesion refers to the quality of relationships among neighbors, as indicated by the frequency of positive interactions, the number of local friends and acquaintances, and people’s sense of community connections (Forkenbrock and Weisbrod, 2001). Walking on local streets is one of the most common ways that residents interact with neighbors and build community cohesion. To the degree that improved walkability increases neighborly interactions and passive surveillance (the likelihood that responsible citizens will notice crime threats, also called eyes on the street), it tends to reduce local crime and residents’ insecurity (Gilderbloom, Riggs and Meares 2015). Gilderbloom, Riggs and Meares (2015) found positive correlations between neighborhood Walk Score rating and housing valuation, and negative correlations with crime and housing foreclosure rates.

Although there is no standard method for measuring these benefits, they are potentially large, including increased happiness and security, reduced crime damages and policing costs, and increased property values and business activity; walkability improvements that significantly increases community cohesion are probably worth hundreds of dollars annually per capita.

Benefits Provided by Automobile Travel Reductions

These benefits are caused by reduced automobile travel, indicated by affected travelers’ reductions in motor vehicle mileage and automobile mode shares.

Reduced Traffic and Parking Congestion

Shifts from driving to walking can help reduce traffic congestion. In dense urban areas where congestion is most serve, a significant portion of vehicle travel (often 10-30%) consists of short trips that could shift to walking. Where walking conditions are poor, people often drive to cross roadways, adding traffic friction. Improving walking conditions can increase public transit travel, which reduces longer vehicle trips, and can reduce chauffeuring trips, which often generate empty backhauls that also contribute to congestion.

There is empirical evidence that walkability improvements reduce traffic congestion. For example, a major study for the Arizona Department of Transportation found significantly less congestion on roads in older, more walkable neighborhoods than in newer, suburban areas due to more local retail, more connected streets, and more transit and nonmotorized travel (Kuzmyak 2012). As a result, residents of older neighborhoods generate less total vehicle travel and drive less on major roadways, reducing traffic congestion.

Some pedestrian improvements can increase motor vehicle traffic delays. For example, converting traffic lanes into wider sidewalks, increased crosswalks and traffic calming, can reduce vehicle travel speeds, although these costs are often partly offset by direct benefits to motorists, such as reduced driver stress and accident risk.

Various studies estimate that motor vehicles imposes congestion costs that average 10¢ to 35¢ per urban-peak vehicle mile, with higher values in dense urban centers (Litman 2009; TC 2006).

Road and Parking Facility Cost Savings

Pedestrian improvements that reduce vehicle ownership and travel can reduce road and parking facility costs. In 2012, U.S. roadway expenditures by all levels of government totaled $221 billion dollars (FHWA 2012), which averages about $650 per capita or 7.4¢ per vehicle-mile. Parking facility costs probably total $2,000-$4,000 annually per capita, assuming 2-4 off-street spaces per person with $1,000 average annual cost (Litman 2009). Although these are durable assets, over the long run, reducing vehicle ownership and use probably provides approximately proportional cost reductions, so a 10% reduction in vehicle ownership and use probably saves about $65 per capita in annul roadway costs and $200-400 in per capita annual parking facility costs, with larger savings for reductions in urban-peak travel reductions that reduce the need to expand expensive urban highways and structured parking.

If no other data are available it is reasonable to assume that each automobile vehicle-mile reduced typically saves 7.4¢ in roadway costs, and each automobile commute trip reduced saves $5 in parking facility costs, with higher values for reductions in urban-peak travel.

Consumer Savings and Affordability

Walking can substitute for vehicle travel directly, and provides access to public transit which provides additional vehicle travel reductions. As a result, improved walkability allows households save on vehicle expenses. Since these savings can be particularly large for lower-income people, walkability improvements tend to increase affordability.

At a minimum, reducing vehicle travel provides operating cost savings, which typically total about 10-20¢ per vehicle-mile. In addition, depreciation and insurance costs are partly variable, since increased driving increases the frequency of vehicle repairs, reduces vehicle resale value, and increases traffic crash and citation risks. These mileage-related costs typically average 10-15¢ per mile, so savings total 20-35¢ per vehicle-mile reduced (Litman 2009). If walkability improvements allow households to reduce their vehicle ownership, they can typically save $2,000 to $4,000 in annual vehicle and residential parking costs (Polzin, Chu and Raman 2008).

Walking may increase equipment and fuel costs. Functional shoes typically cost $50-100 per pair and last 1,000-2,000 miles, or 3-10¢ per walk-mile, although marginal costs are generally smaller since consumers often replace shoes before they wear out, for fashion sake. Walking requires food for fuel, which is more costly than gasoline per calorie, but the amounts are generally small (a 150 pound person burns 80 calories per mile walked, about the energy in a slice of bread), and many people consume more calories than optimal so increased energy consumption is a benefit rather than a cost.

Reduced Chauffeuring Burdens

Chauffeuring refers to additional vehicle travel required to transport non-drivers. In automobile-dependent communities, 5-15% of total vehicle travel consists of chauffeuring (Litman 2015). Many chauffeuring trips require empty backhauls, so transporting a passenger one mile generates two vehicle-miles of travel. This imposes drivers’ travel time and vehicle operating costs. By improving non-drivers travel options, walkability improvements can reduce chauffeuring burdens and associated costs.

Reduced Crash Risk

Walking tends to have higher crash casualty rates per mile than driving, which suggests that shifts from driving to walking increase risk, but this is offset by reductions in total travel (a short walking trip often replaces a longer automobile trip), reduced risk to other road users, and more cautious driving in areas with more walking (Rabl and de Nazelle 2012). Many common traffic safety strategies, such as graduated driver’s licenses, special testing of senior drivers, and anti-impaired-driving campaigns, are intended to reduce driving by particular groups. Their effectiveness depends on travellers having suitable alternatives to driving, which requires good walkability. As a result, a community’s total per capita traffic casualty rate tends to decline as walking increases, a phenomenon called “safety in numbers” (Jacobsen 2003).

Since traffic injuries and deaths are among the largest of transportation costs, increasing traffic safety can provide large benefits. This impact can be evaluated using models that predict the safety impacts of vehicle travel reductions (Turner, Roozenburg and Francis 2006) and crash costs (Blincoe, et al. 2014). In a typical situation, external crash costs savings (costs to other road users) are worth an average of 5-10¢ per vehicle-mile reduced (Litman 2009).

Energy Conservation

Energy consumption imposes various external costs, including economic and national security impacts from dependence on imported petroleum, plus environmental and health damages from pollution, so energy conservation can provide various benefits (NRC 2009).

Petroleum consumption external costs are estimated to be 2-5¢ per vehicle-mile (Litman 2009; NRC 2009), and more if all environmental costs are considered, and relatively high values are justified because non-motorized travel substitutes for short urban trips in which motor vehicles are fuel inefficient due to cold starts and congestion.

Pollution Reductions

Motor vehicle production and use result in air, noise and water pollution which harm people, agricultural and the natural environment. Walking produces virtually no pollution. Per mile emission reductions tend to be relatively large when active modes substitute for short urban trips which have high emission rates due to cold starts and congestion.

Various studies quantify and monetize motor vehicle pollution damages, but many of these estimates include only a limited portion of total pollution costs. Automobile air, noise and water pollution costs are typically estimated to average 2¢ to 15¢ per vehicle-mile, with lower-range values in rural conditions and higher values under congested urban conditions, but relatively high values can be justified to reflect the tendency of walking to reduce short urban trips (Litman 2009; Vermeulen, et al. 2004). A British study estimates that shifts from driving to active modes provide air pollution reduction benefits of £0.11 in urban areas and £0.02 in rural areas, with higher values for diesel vehicles (SQW 2007).

Additional Travel Time

Since walking tends to be slower than automobile travel, shifts from driving to walking can increase users’ travel time. However, as previously discussed, walking travel time unit costs vary: under unpleasant conditions (such as walking along a busy roadway without sidewalks) walking has high costs per hour, but under pleasant conditions it has low or negative costs (users considered the extra time spent walking a benefit rather than a cost, because it is enjoyable and provides physical activity that reduces the need to spend special time exercising), so users will choose these modes even if it requires more time (Björklund and Carlén 2012; Litman 2009). Because walking is inexpensive, its effective speed (travel time plus time spent earning money to pay for transport) is often faster than driving (Tranter 2004). These factors are highly variable; a person may one day prefer walking and another day prefer driving. If walkability improvements cause travellers to shift from driving to walking, they must be better off overall, considering all direct benefits and costs, or they would not change.

More Compact Communities

Good walkability is a prerequisite for compact, multi-modal community development, often called Smart Growth. As a result, walkability improvements can help create more compact communities, measured by development density, overall accessibility, and per capita vehicle travel. Where this occurs, often provides various Smart Growth benefits (Burchell and Mukherji 2003; Litman 2014).

Openspace Preservation

More compact development reduces per capita land consumption, which helps preserve openspace, including farmland and wildlife habitat, providing economic productivity and ecological benefits. Although these benefits are difficult to measure, they can be large, particularly in areas with high value farmlands or unique environmental qualities (Litman 2014).

Improved Accessibility

More compact, mixed, multi-modal communities reduce the travel distances required to reach destinations, which improves overall accessibility, which provides various cost savings and benefits. Residents of compact, multi-modal neighborhoods typically own 20-50% fewer vehicles and drive 30-60% fewer annual miles, which reduces traffic and parking congestion, consumer costs, accidents and pollution emissions, compared with sprawled, automobile-dependent development (Frank , et al. 2011; Kuzmyak 2012; Sciara, Handy and Boarnet 2014).

Efficient Public Infrastructure and Services

More compact development typically reduces the costs of providing public infrastructure and services, such as roads, utility lines and emergency services, by 10-50% compared with sprawl development patterns (Burchell and Mukherji 2003).

Economic Development

More compact and multi-modal development tends to increase economic productivity in several ways: it increases overall accessibility which expands the pool of workers available to businesses, provides agglomeration efficiencies, reduces transportation costs, and increases property values and tax revenues per developed acre (Graham 2007; Hsieh and Moretti 2014; Litman 2014).

Walkability improvements and streetscaping often increase local commercial activity (Schlossberg, et al. 2013). Detailed analysis by Hack (2013) indicates that walkable shopping tend to be economically successful, improved walkability tends to increase commercial and residential property values, many people want to live within walking distances of commercial services, and that current market trends are likely to increase demand for walkable shopping districts. Similarly, research by the New York City Department of Transportation found that indicators of economic vitality (sales tax receipts, commercial vacancies, number of visitors) tend to improve after walkability improvements were implemented on commercial streets (NYCDOT 2013). For example, expanding walking facilities in Union Square North (Manhattan) reduced commercial vacancies 49%, converting a curb lane into a public seating area on Pearl Street increased sales volumes at adjacent businesses by 14%.

Conclusions

Walking plays unique and improvement roles in an efficient and equitable transportation system, and walkability improvements can provide many economic, social and environmental benefits. Conventional transportation economic evaluation tends to overlook many of these benefits and so undervalues walking, leading to far less investment in walking than justified by its current or potential mode share, the portion of residents who rely on walking for basic mobility, or based on its total benefits.

Many practitioners, public officials and citizens support active transport, but their efforts often focus on cycling rather than walking (although there are typically twenty walking trips for each cycling trip), and on specific benefit categories such as health and environmental benefits (although these are just two of more than a dozen benefits to consider). Apparently, most stakeholders consider walking too common and its benefits too obvious to deserve serious research; it is simply too pedestrian (pun intended). Advocates need better evaluation tools for walking to receive the full support it deserves.

This chapter provides a framework for comprehensive evaluation of walking benefits and costs. These impacts vary depending on changes walking conditions, walking activity, automobile travel and communities development patterns. As a result, evaluation requires demand models that can accurately predict how a planning decision will affect transport activity and land use development patterns. Older models do this poorly; fortunately, newer models are better. It is important that practitioners understand the omissions and biases in any models they use.

Walkability improvements often leverage additional automobile travel reductions, and associated benefits, beyond just the travel shifted from automobile to walking, so an additional mile walked reduces more than one vehicle-mile traveled. This occurs because walkability improvements support public transit travel, reduce chauffeuring vehicle travel, and can provide a catalyst for more compact, multi-modal neighborhood development. Not every walkability improvement provides these additional benefits; pedestrian facilities that are unconnected or built where there is little demand may provide minimal benefits and no leverage effects, but there is now credible evidence of significant latent demand for walking; surveys and case studies indicate that many people would prefer to drive less and rely more on walking, cycling and public transit, provided that those modes are convenient, comfortable and safe to use. Comprehensive economic evaluation provides guidance for responding to those demands.

Greater appreciation of walking benefits can change planning priorities. It tends to justify more public investment in walking programs and projects, shifting road space from traffic and parking lanes to sidewalks and paths, more traffic calming and traffic speed control, and policies to create more compact and multi-modal communities. These are true win-win strategies which directly benefit people who walk, and indirectly benefit other community members, including motorists who enjoy less traffic and parking congestion, increased safety and reduced chauffeuring burdens.

Table 6 Summary of Non-Motorized Transport Benefits and Costs (Litman 2011)

Impact Category	Description
*Improve NMT Conditions*	Benefits from improved walking and cycling conditions.
User benefits	Increased user convenience, comfort, safety, accessibility and enjoyment
Option value	Benefits of having mobility options available in case they are ever needed
Equity objectives	Benefits to economically, socially or physically disadvantaged people
*Increase NMT Activity*	Benefits from increased walking and cycling activity
Fitness and health	Improved public fitness and health
*Reduced Vehicle Travel*	Benefits from reduced motor vehicle ownership and use
Vehicle cost savings	Consumer savings from reduced vehicle ownership and use
Avoided chauffeuring	Reduced chauffeuring responsibilities due to improved travel options
Congestion reduction	Reduced traffic congestion from automobile travel on congested roadways
Reduced barrier effect	Improved non-motorized travel conditions due to reduced traffic speeds and volumes
Roadway cost savings	Reduced roadway construction, maintenance and operating costs
Parking cost savings	Reduced parking problems and facility cost savings
Energy conservation	Economic and environmental benefits from reduced energy consumption
Pollution reductions	Economic and environmental benefits from reduced air, noise and water pollution
*Land Use Impacts*	Benefits from support for strategic land use objectives
Pavement area	Can reduce road and parking facility land requirements
Development patterns	Helps create more accessible, compact, mixed, infill development (smart growth)
*Economic Development*	Benefits from increased productivity and employment
Increased productivity	Increased economic productivity by improving accessibility and reducing costs
Labor productivity	Improved access to education and employment, particularly by disadvantaged workers.
Shifts spending	Shifts spending from vehicles and fuel to goods with more regional economic value
Support specific industries	Support specific industries such as retail and tourism
*Costs*	Costs of improving non-motorized conditions
Facilities and programs	Costs of building non-motorized facilities and operating special programs
Vehicle traffic impacts	Incremental delays to motor vehicle traffic or parking
Equipment	Incremental costs to users of shoes and bicycles
Travel time	Incremental increases in travel time costs due to slower modes
Accident risk	Incremental increases in accident risk

This table summarizes potential non-motorized transport benefits and costs.

Evaluating Existing Conditions – General Techniques

Field Surveys

Various methods are used to evaluate existing walking and cycling conditions (Moudon and Lee 2003). Planners, community members and public officials can walk around an area to survey of walking and cycling conditions. A Walkable Places Survey, described below, is an organized framework for involving community members in field surveys. Some transportation agencies use volunteers or hired college students to perform field surveys. Below are some of the features that should be evaluated in site surveys. Also see the Level of Quality Guidelines (Burden, 2003), which illustrate how specific roadway conditions affect walking, bicycling, traffic calming, transit access and street crossings.

Field Survey Data to Collect

· Non-motorized traffic volumes and speeds.

· Sidewalk, path, and trail conditions (effective width, surface condition, sight distances, etc.).

· Security, cleanliness, vandalism, litter, and aesthetic conditions.

· Public washrooms and other services along trails and bike routes.

· Curb cuts, ramps and other universal access facilities.

· Pedestrian road crossing facilities.

· Vehicle traffic volumes and speeds.

· Lighting along streets and paths.

· Special hazards to walking and cycling.

· Roadway and road shoulder widths and pavement conditions (for cycling).

· Presence of parked cars adjacent to the traffic lane.

· Presence of potholes and dangerous drain grates.

· Bicycle Parking.

When evaluating facilities it is important to maintain a distinction between nominal (“in name”) and functional (“actual condition”) dimensions. For example, many sidewalks and paths are nominally 1.8 to 2 meters wide, but functionally they may be much narrower, due to objects such as telephone poles and signposts, and surface failures such as cracks and potholes. As a result, a walkway that meets technical specifications may be inadequate for some potential users (particularly wheelchair users and people with strollers). Similarly, a bike lane may be useless if it has poor surface conditions or is frequently used for vehicle parking.

Evaluating Current Policies and Practices

The efficiency of walking and cycling transportation is highly affected by land use factors such as land use mix, street Connectivity, and site design (see Land Use Impacts on Transportation), local street design standards and development ordinances should be evaluated. Current zoning codes, such as minimum parking and lot size requirements, tend to discourage pedestrian-oriented design (New Urbanism), and current planning practices often undervalue non-motorized safety and mobility impacts (Goodman and Tolley 2003).

Transportation plans, planning practices and municipal budgets can be evaluated to determine whether they give non-motorized travel sufficient consideration:

· Do transportation plans include walking and cycling improvement components?

· Do transportation surveys and Models incorporate data on non-motorized travel, including short trips, off-peak trips, non-motorized components of linked trips and travel by children?

· Is there a pedestrian/bicycle planner or program coordinator within the transportation agency?

· Do non-motorized improvements receive adequate funding, taking into account the role of non-motorized as a form of basic mobility, as a complement to transit travel, and as a form of recreation?

Safety

Transportation Risk and Safety can be evaluated in several different ways, which tend to give different conclusions about the relative safety of walking and cycling. When measured per unit of travel (per mile or kilometer), non-motorized travel has about ten times the fatality rate as driving. But the health risk from non-motorized travel is less than these estimates indicate because:

Non-motorized travel imposes minimal risk to other road users.
Non-motorized transport encourages shorter trips and land use patterns that reduce travel distances over the long term. Put another way, when people shift from non-motorized to motorized travel they often increase their total annual travel, and therefore their exposure to crash risk.
Bicycling offers significant Health Benefits that offset accident risk.
As non-motorized travel becomes more common in a community, crash rates tend to decline, apparently because drivers become more cautious and safer facilities are provided.

Empirical evidence indicates that shifts from driving to non-motorized modes can reduce total per capita crash risk. Jacobsen (2003) and Robinson (2003); and Turner, Roozenburg and Francis (2006) found that per capita collisions between non-motorized travelers (pedestrians or cyclists) and motor vehicles decline in areas with higher rates of non-motorized travel suggesting that drivers become more cautious when they see more walkers and cyclists. Jacobsen calculates that the number of motorists colliding with pedestrians and cyclists increases at roughly 0.4 power of the number of people walking or cycling (e.g., doubling NMT travel in a community will increase pedestrian/cycling injuries by 32%), and the probability that a motorist will strike a non-motorized traveler declines with the roughly -0.6 power of the number of people walking and cycling in a community (e.g., as a pedestrian, my risk of being hit by a motor vehicle declines 34% if walking and cycling double in my community). Robinson (2005) found similar results using Australian data: doubling bicycle travel reduces cyclist risk per kilometer by about 34%; and conversely, halving bicycle travel increases risk per kilometer about 52%.

Rojas-Rueda, et al (2011) quantified the overall health impacts to users caused by shifts from urban driving to urban cycling, including increases in accident risk, air pollution exposure and improved public fitness. In this case study, the 181,982 Barcelona residents that use the Bicing public bicycle rental system are estimated to experience 0.03 additional deaths from road traffic accidents, 0.13 additional deaths from air pollution, and 12.46 fewer deaths from improved fitness, resulting in 12.28 annual deaths avoided and a 77 benefit:risk ratio. This does not account for the additional health benefits from reduced accident risk to other road users or reduced air pollution emissions to city residents. The authors conclude that public bicycle sharing schemes can help improve public health and provide other benefits.

Health researchers estimated annual changes in health outcomes and monetary costs expected from reduced local air pollution emissions and improved public fitness if 50% of short trips were made by bicycle during summer months in typical Midwestern U.S. communities (Grabow, et al. 2011) Across the study region of approximately 31.3 million people, mortality is projected to decline by approximately 1,100 annual deaths. The combined benefits of improved air quality and physical fitness are estimated to exceed $7 billion/year. These findings suggest that significant health and economic benefits are possible if bicycling replaces short car trips. Less auto dependence in urban areas would also improve health in downwind rural settings.

The San Francisco Department of Public Health developed an Vehicle-Pedestrian Injury Collision Model which predicts how demographic, geographic and land use planning factors affect the number of collisions resulting in pedestrian injury or death in an area (SFDPH 2008a). The model indicates that pedestrian injuries and deaths increase with motor vehicle traffic volume, vehicle traffic speeds, pedestrian volume, and various intersection and street design factors.

Level of Service Ratings

Transportation facilities have traditionally been evaluated using Level of Service (LOS) ratings that range from A (best) to F (worst, or failure). Multi-Modal Level-of-Service rating systems indicate the convenience and comfort of other modes. Several LOS ratings have been developed for pedestrian and cycling facilities (FHWA 2006; Dowling Associates 2008; Seiff and Weissman 2016). Below are some examples of these ratings.

Walkability Checklist

The Walkability Checklist: How Walkable Is Your Community, by Partnership for a Walkable America and the Pedestrian and Bicycle Information Center (www.walkableamerica.org/checklist-walkability.pdf), provides an easy-to-use form for evaluating neighborhood walkability.

Bikeability Checklist

The Pedestrian and Bicycle Information Center (www.bicyclinginfo.org) produced a community bikeability checklist (www.walkinginfo.org/cps/checklist.htm). It includes ratings for road and off-road facilities, driver behavior, cyclist behavior, and barriers, and identifies ways to improve bicycling conditions.

Shared-Use Path Level-Of-Service Ratings

The U.S. Federal Highway Administration (FHWA 2006) developed a Shared-Use Path LOS (SUPLOS) model, which is a mathematical formula that uses select inputs describing conditions along a trail to calculate an LOS score. This is based on detailed research that included the creation of path traffic flow theory, an extensive effort to collect data on path operations, and a survey during which path users expressed their degree of satisfaction with the paths shown on a series of videos.

The resulting method requires minimal input and produces a simple and useful result. The method requires only four inputs from the user: One-way user volume in the design hour, mode split percentages, trail width, and presence or absence of a centerline. Factors involved in the estimation of an LOS for a path include the number of times a typical bicyclist meets or passes another path user and the number of those passes that are delayed. The method considers five types of path users when calculating adult bicyclists' LOS, including other adult bicyclists, child bicyclists, pedestrians, runners, and in-line skaters. The FHWA provides step-by-step instructions on how to use the LOS procedure and spreadsheet calculation tool.

The basic SUPLOS model equation is (see the guidebook for more detailed information and cautions on using this method):

SUPLOS = 5.446 – 0.00809(E) – 15.86(RW) – 0.287(CL) – (DPF)

Where:

E = Events = Meetings per minute + 10 (active passes per minute)

RW = Reciprocal of path width (i.e., 1/path width, in feet)

CL = 1 if trail has a centerline, 0 if trail has no centerline

DPF = Delayed pass factor

The SUPLOS model generates a LOS score between zero and five.

The resulting SUPLOS scale can be converted to letter grades. An A is the highest score, excellent, and an F is the lowest score.

LOS Score

X ≥ 4.0 = A

3.5 ≤ X < 4.0 = B

3.0 ≤ X < 3.5 = C

2.5 ≤ X < 3.0 = D

2.0 ≤ X < 2.5 = E

X < 2.0 = F

Interpreting LOS grades

A: Excellent. Trail has optimum conditions for individual bicyclists and retains ample space to

absorb more users of all modes, while providing a high-quality user experience. Some newly

built trails will provide grade-A service until they have been discovered or until their

ridership builds up to projected levels.

B: Good. Trail has good bicycling conditions, and retains significant room to absorb more users,

while maintaining an ability to provide a high-quality user experience.

C: Fair. Trail has at least minimum width to meet current demand and to provide basic service

to bicyclists. A modest level of additional capacity is available for bicyclists and skaters;

however more pedestrians, runners, or other slow-moving users will begin to diminish LOS

for bicyclists.

D: Poor. Trail is nearing its functional capacity given its width, volume, and mode split. Peakperiod

travel speeds are likely to be reduced by levels of crowding. The addition of more

users of any mode will result in significant service degradation. Some bicyclists and skaters

are likely to adjust their experience expectations or to avoid peak-period use.

E: Very Poor. Given trail width, volume, and user mix, the trail has reached its functional

capacity. Peak-period travel speeds are likely to be reduced by levels of crowding. The trail

may enjoy strong community support because of its high usage rate; however, many

bicyclists and skaters are likely to adjust their experience expectations, or to avoid peakperiod

use.

F: Failing. Trail significantly diminishes the experience for at least one, and most likely for all

user groups. It does not effectively serve most bicyclists; significant user conflicts should be

expected.

Highway Capacity Manual

The Highway Capacity Manual published by the Transportation Research Board is a basic reference widely used by transportation planners and engineers for evaluating roadway conditions. It provides Level-of-Service ratings for roads and intersections based on traffic density (an indicator of congestion). The same approach was applied to pedestrian Level-of-Service ratings, as illustrated in Figure 4, but this has been criticized as being inadequate, since it is based on just one variable.

Figure 4 Pedestrian Level of Service (TRB 1997)

This figure illustrates an early pedestrian LOS ratings based only on density. This has been criticized as being too simplistic.

The 2000 Highway Capacity Manual incorporates a number of factors into pedestrian Level-of-Service ratings for roadway crossings, reflecting pedestrian delay, as indicated in Table 7. Kim, et al. (2008) evaluate the impacts that various types of street furniture (benches, bicycle racks, planter boxes, trees, mail boxes, brochure bins, trash cans, vending and coffee carts, and tables and chairs) have on pedestrian level-of-service, depending on their type, size, shape and use. They recommend specific design and management practices based on type of furniture, sidewalk width, pedestrian volumes, and the potential number of users or customers.

Table 7 Pedestrian Road Crossing Level of Service (Milazzo, et al, 1999)

Level of Service	Signalized Intersection	Unsignalized Intersection	Likelihood of Pedestrian Noncompliance
A	<10	< 5	Low
B	10-20	5-10
C	20-30	10-20	Moderate
D	30-40	20-30
E	40-60	30-45	High
F	60+	45+	Very High

Average Delay Per Pedestrian in Seconds. Crosswalk walking speeds are estimated at 1.2 meters per second for most areas, and 1.0 m/s for crosswalks serving large numbers of older pedestrians.

Dixon

Dixon (1996) describes LOS ratings for walking and cycling conditions. The ratings take into account the existence of separated facilities, conflicts, speed differential, congestion, maintenance, amenities, and TDM. These are relatively easy to use methods for evaluating non-motorized roadway conditions that may be more practical than other methods that are more data intensive.

Tables 8 and 9 summarize a simplified method for evaluating walking and cycling level-of-service. The results are then scored to determine the LOS rating in Table 10.

Table 8 Pedestrian Level-of-Service (Dixon, 1996)

Pedestrian

Points

Facility

(Max. value = 10)

Not continuous or non-existent

Continuous on one side

Continuous on both sides

Min. 1.53 m (5’) wide & barrier free

Sidewalk width >1.53 (5’)

Off-street/parallel alternative facility

Conflicts

(Max. value = 10)

Driveways & sidestreets

Ped. Signal delay 40 sec. or less

Reduced turn conflict implementation

Crossing width 18.3 m (60’) or less

Posted speed

Medians present

0.5

Amenities

(Max. value = 2)

Buffer not less than 1m (3’5”)

Benches or pedestrian scale lighting

Shade trees

0.5

Motor Vehicle LOS

(Max. value = 2)

LOS = E, F, or 6+ travel lanes

LOS = D, & < 6 travel lanes

LOS = A, B, C, & < 6 travel lanes

Maintenance

(Max. value = 2)

Major or frequent problems

Minor or infrequent problems

No problems

-1

TDM/Multi Modal

(Max. value = 1)

No support

Support exists

Table 9 Bicycle Level-of-Service (Dixon, 1996)

Bicycle

Points

Facility

(Max. value = 10)

Outside lane 3.66 m (12’)

Outside lane 3.66-4.27m (12-14’)

Outside lane >4.27m (14’)

Off-street/parallel alternative facility

Conflicts

(Max. value = 10)

Driveways & sidestreets

Barrier free

No on-street parking

Medians present

Unrestricted sight distance

Intersection Implementation

0.5

Speed Differential

(Max. value = 4)

>48 KPH (>30 MPH)

40-48 KPH (25-30 MPH)

24-30 KPH (15-20 MPH)

Motor Vehicle LOS

(Max. value = 2)

LOS = E, F, or 6+ travel lanes

LOS = D, & < 6 travel lanes

LOS = A, B, C, & < 6 travel lanes

Maintenance

(Max. value = 2)

Major or frequent problems

Minor or infrequent problems

No problems

-1

TDM/Multi Modal

(Max. value = 1)

No support

Support exists

Table 10 Level of Service Ratings

LOS Rating	Points
A	>17
B	>14-17
C	>11-14
D	>7-11
E	>3-7
F	3 or less.

Bicycle Compatibility Index

Harkey, et al (1998) describes the Bicycle Compatibility Index, a practical tool for evaluating the suitability of urban and suburban roadways for cycling. It incorporates curb lane width, traffic volumes and traffic speeds. The report describes how to gather the necessary data and apply the method, discusses case study examples, and includes a spreadsheet model to facilitate the analysis. Tables 11 and 12 show how this information is evaluated to indicate cyclist stress level and suitability ratings for a specific roadway.

Table 11 Roadway Cyclist Stress Level Values

Stress Rating	Speed	Volume	Trucks	Curb Lane	Hindrances
	Posted speed limit (km/hr)	Vehicles/hr per traffic lane	Percentage of truck traffic	Curb lane width (m)	Commercial driveways and intersections per km
1	<40	<50	<2%	>4.6	<6
2	50	51-150	4%	4.3	13
3	60	151-250	6%	4.0	19
4	65	251-350	8%	3.7	25
5	>75	351-450	>10%	<3.3	>31

These values are used to calculate Cycling Suitability Rating in Table 8.

Table 12 Cycling Suitability Rating

Summed Values	Average Stress Level	Road Suitability for Cycling
< 7	1	Road is reasonably safe for all types of cyclists.
7-12	2	Road accommodates casual and experienced cyclists, but needs improvement to accommodate child cyclist.
13-17	3	Road accommodates experienced cyclists, but needs improvement to accommodate casual and child cyclists.
18-22	4	Needs improvements to accommodate experienced cyclists, not recommended for casual and child cyclists.
>22	5	May be unsuitable for all cycling.

This table indicates a roadways Cycling Suitability Ratings, and the type of cycling that it can accommodate.

Additional factors considered in the Bicycle Compatibility Index are listed below:

· Presence of bicycle lane or paved shoulder.

· Bicycle lane or paved shoulder width.

· Curb lane width.

· Curb lane volume.

· Other lane volume.

· Average traffic speed.

· Presence of parking lane with more than 30% occupancy.

· Type of roadside development.

· Truck volumes.

· Parking turnover.

· Right turn lanes.

Bikability Evaluation (McNeil 2010)

McNeil (2010) evaluates neighborhood bikeability based on the goods and activities that can be reached within a 20-minute bike ride, taking into account the quality of cycling infrastructure and the location of destinations such as stores, schools and parks. This information can be used to calculate a Bikeability Score ranging from 0 (worst) to 100 (best). Bicycle facility improvements and more local services increase bikeability scores.

Community Cycling Conditions

Most cycling Level-of-Service rating systems are designed to evaluate conditions on a specific road. Other systems evaluate overall cycling conditions in a community. IHT (1998) describes how to perform a cycle audit and cycle review, which are standard frameworks for evaluating roadway conditions and transportation plans in terms of their suitability for cycling.

Below are some additional factors to consider when evaluating the quality of cycling in a community.

Cycling rates (per capita annual cycling trips or cycling mode split).
Cycling fatality and injury rates.
Availability and quality of Bicycle Parking and changing facilities.
Bike/Transit Integration, including the ability to carry bikes on transit vehicles, and the availability of bike parking and rental services at train and bus stations.
Quality of Bicycle Education and Encouragement Programs.
Degree to which common destinations (schools, commercial centers, transit stations, etc.) are located and designed for cycling access.

Acceptable Walking Distance

The distance that people are willing to walk is often an important factor in transportation and land use planning. It determines the optimal size of a commercial district or urban village, the area served by a particular public transit service, and the acceptable distance between parking facilities and destinations. This can be called a walkable area or a ped-shed (based on watershed).

The table below indicates Level of Service ratings for pedestrian access. For typical urban conditions, LOS A is less than one block, LOS B is 1-4 blocks, LOS C is 4-8 blocks, and LOS D is more than 8 blocks between a destination and its parking facilities.

Table 13 Level of Service By Walking Trip Distance (in Feet) (Smith and Butcher, 1997)

Walking Environment	LOS A	LOS B	LOS C	LOS D
Climate Controlled	1,000	2,400	3,800	5,200
Outdoor/Covered	500	1,000	1,500	2,000
Outdoor/Uncovered	400	800	1,200	1,600
Through Surface Lot	350	700	1,050	1,400
Inside Parking Facility	300	600	900	1,200

Acceptable walking distances are affected by degree of weather protection, climate, line of site (whether pedestrians can see their destination), and “friction” (interruptions and constraints along the way, such as cross traffic). Of course, people’s abilities and preferences also vary; some may be able to walk much farther than others, so it is important to accommodate people with mobility constraints in pedestrian planning. For example, it may be appropriate to reserve some parking spaces close to destinations for people with disabilities and delivery vehicles, so they have shorter walking distances.

Walking Speeds

Walking speeds are an important factor in the design and management of walking facilities, particularly traffic signals. Pedestrian signals that provide inadequate crossing time can create danger and discomfort for slower pedestrians. Walking speeds vary depending on various demographic and geographic factors. Healthy adults typically walk 4.0 feet (1.2 meters) per second or faster, but lower walking speeds are common for older and younger people, people with disabilities, people carrying baggage or pushing handcarts, or when walking on rough surfaces or up a hill. The Institute of Transportation Engineers recommends that traffic signal timing be based on maximum walking speeds of 3.0 feet (0.9 meters) per second to safely accommodate slower pedestrians (LePlante and Kaeser 2004).

Krizek, et al. (2007) developed methods for calculating travel times by walking, cycling and public transit modes. The researchers used information on networks and speeds to construct a series of maps that graphically depict various non-auto travel networks at different points in time between 1995 and 2005. The maps break down origins and destinations into several zones (similar to watersheds). This technique makes it possible to see changes in travel time between different “travel-sheds” over time.

Walking Security Index

Wellar (1998) uses a Walking Security Index to evaluate pedestrian crossing conditions at roadway intersections, taking into account a wide range of variables that affect pedestrian safety, comfort, and convenience, as summarized in Table 14. This indicates that increased road width, traffic volumes, traffic speeds, vehicle mix, and various other factors affect the mobility, safety and comfort of pedestrian travel.

Table 14 Walking Security Index Variables

Infrastructure

Vehicle Traffic

Pedestrian

Performance

Behavior

1. Number of lanes.

2. Speed

3. Grade (incline).

4. Turning lanes.

5. Curb cut at intersections.

6. Stop bar distance from crosswalk.

7. Sight lines

8. Peak vehicle volumes.

9. Vehicle types.

10. Trip purpose.

11. Turning movements.

12. Pedestrian volumes.

13. Pedestrian age.

14. Right-turn-on-red.

15. Signage.

16. Ice/snow/slush removal.

17. Pedestrian-vehicle collisions.

18. Pedestrian-vehicle conflicts.

19. Vehicle moving violations.

Other researchers emphasize that street crossings must be properly located, that is, they must follow pedestrians’ natural walking patterns and sight lines, without requiring extra walking distance (Stonor, Campos and Smith 2001). “Faced with a badly located and badly designed crossing, pedestrians often do one of two things: either they do not cross, and remain instead on one side of the road (with economic consequences for two-sided retailing) or they cross, but do not use the crossing (with road safety consequences).”

Barrier Effect (Severance)

The Barrier Effect (also called Severance), refers to the tendency of roads and traffic to create a barrier to non-motorized travel (Litman 2009). Severance usually refers only to the impacts of a highway facility itself, while the barrier effect refers to the combined impacts of the roadway and vehicle traffic, and so increases with traffic volumes. It represents a degradation of the pedestrian and bicyclist environment that reduces the viability of these modes, often leading to increased driving. This is not to imply that drivers intentionally cause harm, but rather that such impacts are unavoidable when fast, heavy and hard vehicles share space with vulnerable road users. Although it could be argued that impacts are symmetrical, because non-motorized modes cause traffic delays to motorists, pedestrians and cyclists impose minimal accident risk, noise and dust on motorists so the costs they bear are inherently greater than the costs they impose.

Walkability

Walkability reflects the overall support for pedestrian travel in an area. Walkability takes into account the quality of pedestrian facilities, roadway conditions, land use patterns, community support, security and comfort for walking. Walkability can be evaluated in various ways and at various scales. At a site scale, walkability is affected by the quality of pathways, building accessways and related facilities. At a street or neighborhood level, it is affected by the existence of sidewalks and crosswalks, and roadway conditions (road widths, traffic volumes and speeds). At the community level it is also affected by land use Accessibility, roadway Connectivity, such as the relative location of common destinations and the quality of connections between them. Walkability takes into account:

· Pedestrian network quality (quality of paths, sidewalks, street crossings).

· Pedestrian network connectivity (how well sidewalks and paths are connected, and how directly pedestrians can travel to destinations).

· Security (how safe people feel while walking).

· Density and accessibility (distance between common destinations, such as homes, shops, schools, parks).

For example, a busy suburban arterial can have a high pedestrian LOS rating, provided it has sidewalks and pedestrian crossings at intersections, although walking is actually quite difficult and impractical as a form of transportation due to the wide road widths and dispersed land use patterns. Walkability can be enhanced by increasing Clustering and land use mix, by creating pedestrian shortcuts and mid-block pedestrian connections, and by locating commercial buildings close to the sidewalk, rather than being set back behind large parking lots. Walkability is also concerned with the ability to stop in the public right-of-way, for example, to rest, enjoy a viewpoint or shop window, have a conversation or play. Pedestrian Level of Service standards do not encompass these factors, although they are critical to the overall utility of walking as a form of transport.

WalkScore (www.WalkScore.com) automatically calculates a neighborhood’s walkability rating by identifying the distance to public services such as grocery stores and schools. It uses Google maps and business listings. It works for any street address in the United States of America and Canada, assigning points based on the distance to local amenities, then averages the score. Numerous studies (www.walkscore.com/professional/walkability-research.php) have investigated the relationships between WalkScore and various travel, health and economic outcomes.

Rendall, et al. (2011) quantify Active Mode Accessibility (AMA), defined as the proportion of activities that can be reached by active modes (walking, cycling, and public transport) alone, given the population demographics of the study area. AMA is characterized by the underlying geographic form of an urban area and its transport networks. They describe methods for calculating the AMA and apply it to case studies.

The American Podiatric Medical Association (APMA 2007) and Prevention Magazine evaluate the walkability of major cities using the criteria listed in the table below, with a weight of 4 being the most walkable and 1 being the least walkable.

Table 15 Best Walking Cities Evaluation Criteria

Weight	Criteria	Criteria Detail
4	Expert Ratings	Professionals from six organizations devoted to making communities more walkable rated cities that they are familiar with based on a 4 to 1 scale, with 4 being the most walkable and 1 being the least walkable.
3	Walking Commuters	Percentage of residents who walk to work.
3	Green Space	The number of local, state, and national parks per square mile. Studies show that people are more likely to walk if they have safe, aesthetically pleasing places to walk nearby.
3	Safe Streets	The incidence of violent crimes (murder, rape, robbery, and assault) based on population. A score of 100 is the national average--anything under 100 is below the national average and anything above it is higher than the national average.
2	Pedestrian Fatalities	Number of pedestrian fatalities based on population.
2	Fitness Walking	Percentage of the population who walk for exercise.
2	Schools	Number of schools per square mile. Sidewalks and streets around schools tend to be more walker-friendly than other areas, so the more schools a city has the more walker-friendly areas there are.
2	Mass Transit	Percentage of the population who use mass transit as a form of transportation. Cities with more public transportation tend to be more walker-friendly.
2	Cars	Total number of cars per household. The more walkable a city is the less likely its residents are to need a car.
1	WalkScore	The number of destinations such as restaurants, stores, parks, libraries, theaters, and fitness centers within a walkable distance from the center of town as calculated by Walkscore.com, a website that rates cities’ walkability from 0 to 100, with 100 being best.
1	Air Quality	Based on measures from the Environmental Protection Agency and weather data from government sources, cities received index scores with 100 being the national average for air quality. Similar to the crime rating, lower scores are below the national average and higher scores are above the national average.
1	Rails to Trails Program	Cities that have railroad tracks that have been converted to walking or cycling trails within their county make walking a priority. Thus, cities that participate in the Rails to Trails program received a credit in our rating system.
1	Cleanliness	Cities that participate in the Keep America Beautiful program, which is a nonprofit dedicated to making the nation’s communities cleaner and greener, received a credit in our rating system.

Leinberger (2007) defines walkable urban areas as having the following features:

At least five times as dense as drivable sub-urban (floor-area-ratio of between 0.8 and upwards to 40.0).
Mixed-use (residential, office, retail, cultural, educational, etc.).
Compact (regional-serving walkable urban places, as defined below, are generally between 100 and 500 acres in size).
Generally accessible by multiple transportation means (transit, bike, car and walking).
Walkable for nearly every destination once in the place.

Loukopoulos and Gärling (2005) find that on average people will drive rather than walk for a distance over 1,236 meters, with higher walking thresholds for women, and people who frequently walk, and lower values for more difficult walking conditions and people who frequently drive. The authors conclude that improving walking conditions and marketing campaigns can decrease the frequency of short automobile trips.

Defining “Walkable Community”

By Dan Burden of Walkable Communities (www.walkable.org).

A “walkable community” is designed for people, to human scale, emphasizing people over cars, promoting safe, secure, balanced, mixed, vibrant, successful, healthful, enjoyable and comfortable walking, bicycling and human association. It is a community that returns rights to people, looks out especially for children, seniors and people with disabilities and takes aggressive action to reduce the negative impacts of sixty-plus years of auto-centric design and uncivil driving practices. It is also a community that emphasizes economic recovery of central neighborhoods, promotes the concepts of recovering and transforming suburban sprawl into meaningful villages, and especially takes ownership and action to protect and preserving open space.

A walkable community, like a livable community, smart growth community, or sustainable community, makes a neighborhood, hamlet, village, town, city or metropolis into a place where many people walk, ride bicycles and use transit, and where anyone who drives a car moderates their behavior in a way where they take nothing from the rights of those who wish to stay healthy and active by taking part in activities outside the car.

A walkable community is one that is old, historic, well worn, restored sensibly and worthy of protection. A walkable community is one that is compact, new, fresh, invigorating and teaming with people enjoying their streets, parks, plazas, buildings and other physical space.

Below are ten indicators of prosperous, walkable, healthy and livable communities:

Compact, lively town center.
Many linkages to neighborhoods.
Low speed streets.
Neighborhood schools and parks.
Public places packed with children, teenagers, adults and people with disabilities.
Convenient, safe and easy street crossing.
Inspiring and well-maintained public streets.
Land use and transportation mutually beneficial.
Celebrated public spaces and public life.
Many people walking.

Also see “Key Principles of Building Healthy Communities,” Building Communities With Transportation: Distinguished Lecture Presentation, Transportation Research Board, Walkable Communities (www.walkable.org/download/TRBpaper.doc), January 10, 2001.

Nabors, et al. (2007) review various methods for evaluating walkability. Some of these methods are described below.

Walkability Audit Tool

CDC (2004) provides a walkability audit tool which consists of an evaluation form, shown below, for rating a particular travel segment or area in terms of eight factors, with higher weights for factors considered more important. A total rating of 70-100 is considered good, ratings of 40-69 are considered medium, and a rating under 40 is considered poor.

Table 16 Walkability Audit Tool

A. Pedestrian Facilities (High Importance): Presence of a suitable facility, such as a walking path or sidewalk.

No facility – pedestrians walk on road or dirt path.

Paved walkway on one side of road, minor discontinuities that present modest barrier to walking.

Continuous paved walkway on both sides of road or completely separated from roadway.

B. Pedestrian Conflicts (High Importance): potential for conflict with motor vehicle traffic due to driveways, high speed and volume traffic, large intersections, poor pedestrian visibility, etc.

High conflict potential

Low conflict potential.

C. Crosswalks (High Importance) presence and visibility of crosswalks at intersecting roads. Traffic signals have functional ‘walk’ lights that provide sufficient crossing time.

Crosswalks not present despite large intersections.

No intersections, or crosswalks clearly marked

D. Maintenance (Medium Importance): buckling pavement, overgrown vegetation, standing water, etc.

Major or frequent problems.

No problems.

E. Path Size (Medium Importance): adequate functional width, taking into account factors such as utility poles and signs within pathway.

No permanent facilities.

Narrow path

(<3’ width).

Wide path

(>5’ functional width).

F. Buffer (Medium Importance): space separating path from adjacent roadway

No buffer from roadway or pedestrians walk in roadway.

Moderate buffer

(3’ from traffic)

Not adjacent to roadway.

G. Universal Access (Medium Importance): ease of access for mobility impaired people. Includes ramps for wheelchairs, handrails along steps, etc.

Completely impassible to people with impairments.

Difficult or dangerous (e.g., no wheelchair ramps).

Accessible, but inconvenient (e.g., greater travel distance)

Fully accessible and convenient.

H. Aesthetics (Medium Importance): attractive facilities and conditions create a place that people enjoy.

Uninviting

Very attractive.

I. Shade/Covering (Low Importance): amount of shade and rain cover.

No cover

Moderate cover

Full cover

This form can be used to evaluate the walkability of a particular travel segment or area. Values for “High Importance” factors (A-C) are multiple by 3, and “Medium Importance” factors (D-H) are multiplied by 2. The results are summed for a total score.

Calculations

	Basic Sums	Weight Factor	Totals
Sum of “High Importance” Factors (A-C)		x 3
Sum of “Medium Importance” Factors (D-H)		x 2
Sum of “Low Importance” Factor (I)		x 1
Total Score			/100

Observations

In addition to the quantitative analysis, also provide the following information.

What is the most dangerous location along this segment or area?

What is the most unpleasant element of this segment or area?

What improvements would make this segment or area better for walking?

Would it be possible to design a more direct route to connect destinations along this segment or area?

Are conditions of this segment or area appropriate and attractive for exercise and recreational use?

Walkshed: One Mile From Home

Alan Durning, Cascadia Scorecard Weblog (http://cascadiascorecard.typepad.com/blog), 5 April 2006

A one-mile perimeter defines our family’s pedestrian travel zone—call it our “walkshed.” Fortunately, because we chose to live in a compact community, our walkshed turns out to be well stocked.

We can stroll to scores of shops and services—248 to be precise. I know because I counted. You can, too, in less than 60 seconds. I’ll tell you how in a moment.

Among the establishments in our domain are a bowling alley, a produce stand, a movie theater, and a hardware store, plus public institutions such as our post office, swimming pool, farmers’ market, and skate park (new and very cool!).

We’ve got pairs of independent booksellers, thrift stores (we know them well), and bakeries (ditto). Three pharmacies, three yoga studios, and three video stores offer us medication, meditation, and mesmerization, respectively. Five grocers and six dry cleaners compete for our appetites and our wrinkles. Nine barbers eye our locks. Dozens of specialty shops hawk their curiosities in the range of our Burley: one sells only flags, another only gifts from Norway, a third only old magazines.

True coffee houses number six, only one of them a Starbucks (which, because it's so low, may be the most surprising number in this tally). Restaurants? We’re provisioned with 54! (And there are 151 within two miles: we’ll walk farther for great eating.)

Two neighborhood ice creameries are counteracted by an astonishing 42 dentists (none of them covered by our insurance, sadly). Two local smoke shops are outnumbered by an even more astounding 74 doctors (again, not covered by our insurance). And then there’s our one neighborhood orthodontist: he has straightened or is straightening all three of our kids’ teeth, for which we've paid him enough to buy three used Volvos or most of a new Prius.

I should perhaps note that, despite these large counts, we do not live downtown. Far from it—-in fact, five miles from it. Our neighborhood of Ballard is a typical streetcar community developed largely in the 1920s and replicated in every North American city of similar age. I should also probably note that our neighborhood is definitely not Mayberry. It's got 44 auto shops, 10 taverns, and a liquor store. Oh, plus two sex-toy shops and two strip clubs. (Or so the signs say -- I’ve never been inside. I swear.)

All of these counts I did in my head or using the yellow pages, and you can do the same for your home if you live in the United States. Here’s how: To get the a fairly complete count of businesses, go to this Qwest online phone directory (www.dexonline.com) select the business listings, type “all” in the category field, click “near a street address,” type in your address, and choose “1 mile.” (Sorry, Canadians, I have yet to find a .ca that performs this trick.) If you’re lucky and the database gods are smiling on you (the site is temperamental), Qwest will promptly reveal how many businesses there are within a one-mile walk of your front door. Call this your Walkshed Index.

Ours, as I said, is 248. There are two hundred and forty eight places where my family can do business within a mile of home, not counting public facilities. That number is not remarkably high: the walkshed index at my downtown office address is 6,623. Nor is it remarkably low: one suburban family I know has a score of 0. But it means that living car-free is more viable for us than it would be for many families.

Walkability Urban Design Qualities

The Measuring Urban Design Qualities Illustrated Field Manual (Ewing, et al, 2006) provides a functional methodology for quantifying urban design qualities related to walkability, that can be used to evaluate walkability and identify ways of improving walking condition. It defines and evaluates five specific urban design qualities: imageability, visual enclosure, human scale, transparency, and complexity. The method uses statistically-derived equations that link objectively measured physical features of the environment to ratings of urban design qualities. To aid in the dissemination of the measures, a field survey instrument, computer spreadsheets and a training manual have been developed for use by researchers in their efforts to study relationships between the built environment and walking behavior.

Global Walkability Index

Krambeck and Shah (2007) developed the Global Walkability Index to rank cities within a country and across the world based on the safety, security, and convenience of their pedestrian environments (www.cleanairnet.org/caiasia/1412/article-60499.html). It includes the following components:

· Safety and Security is intended to determine the relative safety and security of the walking environment. For example, what are the odds a pedestrian will be hit by a motor vehicle? What safety measures are in place at major crossings and intersections? How safe from crime do pedestrians feel along walking paths?

· Convenience and Attractiveness reflects the relative convenience and attractiveness of the pedestrian network. For example, do pedestrians have to walk a kilometer out of their way just to cross a major road? Is there sufficient coverage from weather elements along major walking paths? Are paths blocked with temporary and permanent obstructions, such as parked cars or poorly placed telephone poles?

· Policy Support reflects the degree to which the municipal government supports improvements in pedestrian infrastructure and related services. Is there a non-motorized planning program? Is there a budget for pedestrian planning? Are pedestrian networks included in the city master plan?

Neighborhood Walkability Indicators

Bradshaw (1999) lists the following neighborhood Walkability indicators:

· Sidewalk conditions.

· Land use density

· Off-street parking

· Sitting spots

· Chance of meeting an acquaintance

· Age at which children can walk on their own.

· Women’s perception of safety.

· Transit service.

· Number of neighborhood “places of significance.”

· Size and proximity of parks.

A PedNet Internet list task force developed the following indicators of walkability:

· Path surface quality

· Protection from traffic

· Path slope (hills)

· Pathway continuity

· Road crossing delay

· Path congestion

· Pathway legibility (signs, lighting)

· Shade/rain cover

· Security

Walkability Checklists

The National Safe Kids Campaign (www.safekids.org) and AARP (2005) produce walkability checklists for evaluating walking conditions and identifying ways to improve walkability in a neighborhood. The checklist below is based on the National Safe Kids Campaign checklist.

Questions

For each of the following questions rate your area from 0 (worst) to 5 (best).

1. Do you have enough room to walk safely? _____

· Sidewalks or paths started and stopped.

· No sidewalks, paths or shoulders.

· Too much traffic.

· Sidewalks were broken or cracked.

· Sidewalks were blocked with poles, signs, dumpsters, etc.

· Other problem:

2. Was it easy to cross the streets? _____

· Road was too wide?

· Traffic was too fast?

· Traffic signals require too much waiting or provide insufficient time to cross?

· Need crosswalk markings or traffic signals?

· Parked cars block the view of traffic?

· Trees or plants block the view of traffic?

· Need curb ramps?

· Other problem:

3. Did drivers behave well? _____

· Backed out of driveway without looking?

· Did not yield to people crossing the street?

· Turned into people crossing the street?

· Drove faster than is legal or safe?

· Sped up to make it through red lights?

· Other problem:

4. Was it easy to follow safety rules? _____

· Cross at sidewalk or where you could see and be seen by drivers?

· Stop and look left, right and then left again before crossing?

· Walk on sidewalks or shoulders (if no sidewalks) facing traffic?

· Cross with the light?

· Other problem:

5. Was your walk pleasant? _____

· Needs more greenspace (grass, trees, flowers)?

· Scary dogs?

· Suspicious activity?

· Not well lit?

· Dirty, lots of litter or trash?

· Other problem:

How does your neighborhood stack up? Add up the ratings and decide:

Total Rating

21-25 Celebrate. You have a great neighborhood for walking.

16-20 Celebrate a little. Your neighborhood is pretty good.

11-15 OK. Your neighborhood needs work.

6-10 Your neighborhood needs a lot of work. You deserve better.

0-5 Your neighborhood is a disaster area for walking.

Rating System for Walkable, Active Living, Active Transportation

by Dan Burden, Walkable Communities (www.walkable.org)

A true test or audit/evaluation of walkability should be expanded to a livability and active living, active transportation components. Older, historic neighborhoods often met this test naturally. Today it is often essential to add back missing elements to these original neighborhoods. It is also essential to write new code in many places to guide developers in creating new neighborhoods achieving livability and active living qualities.

In this test or evaluation would be the ability for a home buyer, realtor, lay person, organization, developer or planner to test the following:

Location of Parks, Plazas and Open Space. Pleasing, useful, public space should be located within 800 feet of all homes (or at least 90% of homes). These areas also meet essential needs of creating informal gatherings, ways to promote neighborhood alliances and exchange, warm places for sun worshipers, physical separation between houses, and more.

Adequacy of Walkways. Walkways should include minimum 5 foot widths, preferably separated from curbs, and cover easily 80% of homes and 100% of all principle streets. Walkways are not always needed when speeds on area streets are 20 mph or lower, and when traffic is well dispersed. (I just found one neighborhood I like in the Aspen area that has 100% of homes on low speed streets). Bonuses are given when alleys accommodate utilities and driveways, making many sidewalks more pleasant and walkable.

High Connectivity. Blocks are well connected, often with 400-600 foot long blocks (or less), or other links, trails and connections make up for block length deficits. Streets need not be in a grid form, but must be short enough to allow ease in reaching most areas readily by foot. Many connections are made to primary streets, schools, and important parks.

Homes Are Oriented to Street. Homes along 90% or more of streets (and all principle streets) have pleasing architecture, variety, personality, character and charm, with many “eyes facing streets”. There is an absence of snout garages. Housing setbacks are typically 15-25 feet, and rarely as much as 40-60 feet.

Homes are Clustered. Bonuses are given if 20% or more of housing stock clusters these units around open courts, a “close”, or park space. These can be both single family and multi-family housing. Such designs allow many children and families to play, all under watchful eyes.

Sufficient Density. Homes, including accessory units, achieve built densities of at least 6-7 du/a. Bonuses are given when densities reach 10-11 du/a or higher. With low densities insufficient numbers of people are out walking to achieve a feeling of security and watchfulness.

Green and Landscaping. Neighborhoods have quality streetscapes and corner treatments. Individual yards of pride invite casual walking. As a test, at least 15% of individual front yard space is devoted to landscaping. If fencing is used it is below 4 foot height and either semi-transparent or highly transparent. There are no chain link fences or solid walls facing streets. Privacy fencing is permitted only on side lots back from front portions of housing. Any “privacy fencing” (walls or non-transparent material) is attractive and on longer/deeper than 60 feet. Bonuses are given when street trees (or other appropriate treatment in arid regions are used) to achieve true canopies or other natural enclosure over 80% of neighborhood streets.

Slow Speed Streets. Most streets, lanes, alleys and avenues maintain low speeds (preferably 15-25 mph). There is an absence of band-aids, such as speed humps trying to corral speeds created by inappropriate street designs. Speed reductions are handled through tasteful widths (narrowings), curb extensions, tree canopies, mini-circles and other modern traffic calming tools. Although avenues (collectors) are permitted to operate at 30-35 mph, designs are pleasant enough to allow all homes to face the street and provide comfort, efficiency, security and welcome. Avenues have added mechanisms supporting bicycling, such as bike lanes.

Neighborhood Schools. A majority of children in the neighborhood have access to local schools. Elementary schools are well located, reaching 80% of all children with a walk of no more than 2,500 feet. Middle school may be more distant, reaching 80% all children within a distance of 5000 feet.

Retail and Civic. There are a variety services within 2500 feet of 80% of all homes. These services include 2-3 small stores oriented to neighborhood needs, and at least one formal civic building, such as a library, post office, great park, other learning institutions, community center, or other place where a variety and diversity of people congregate and get to know one another.

Trails. The neighborhood is served by a comprehensive network of multi-use trails, bicycle boulevards, bike lanes and other means to increase the ability to walk and bicycle readily to most locations.

Accessible and Affordable. The neighborhood has strong components of barrier free streets, parks, civic buildings. There is a variety of housing in the neighborhood, allowing people of all abilities and means to find adequate housing. Many seniors live on the same streets with younger families. “Granny flats, accessory units and other innovative housing can be considered as part of the affordability component. Bonuses are given when at least 20% of housing is considered affordable. If exclusionary (seniors only, or retirement housing) exists, it is nicely blended into the neighborhood, where young children can visit grandparents with a short walk or bike ride, and visa versa.

Walkable Places Survey (www.walkableplaces.com)

By Katherine Shriver

Let’s turn the little geographies of nowhere, that surround us everywhere, into places where people want to be. —Katherine Shriver

The Walkable Places Survey (WPS) is a public health and planning tool designed to assess environmental conditions and opportunities for more walking and healthful, equitable transportation access. A WPS project harnesses on-going community design and livable community initiatives as a force for making these communities more activity-friendly. The WPS objective is to implement a community environment assessment process for state and regional program staff and local community sponsors so that people can begin to enjoy more active and healthier lives.

The WPS has been developed as an organized experiential framework for the description, analysis and discussion of public space conditions. As a place-oriented approach to community improvement, common sense and individual experience are at the center of the WPS process. The highlight of the WPS approach is a public event to bring about 40 individuals from local agencies, organizations and a community or neighborhood together for 8-12 hours. Using the informal, healthful and social act of walking, participants jointly experience and evaluate conditions, discuss and critique alternatives, and recommend improvements relating to the walkability of neighborhood places.

The goal of the WPS event is educational outreach about active community environments that stimulates public participation in the planning process. WPS promotes lively discussion and analytical thinking and anyone can do it. Participants walk together, observe and talk about a public space such as a streetscape, intersection, plaza or park. Afterward the walk, participants discuss their experience and observations. Participants find common ground and a shared sense of purpose by experiencing and discussing what makes a place feel welcoming, memorable and attractive—or not. Those without knowledge of urban design or planning principles can not only participate, but also lead the way to needed environmental changes. Participants develop recommendations for improvements by the end of the day. A WPS event refines public thinking about how to redesign those little “geographies of nowhere” into places where people want to be.

Stages of Change Model. Effecting local environmental change requires a series of consensus-building steps among community stakeholders, professionals and elected officials. The WPS approach supports a stages-of-change model, which successful communities have embraced to make community design improvements:

1) Networking. Community stakeholders who care about walkability in their neighborhoods, schools or worksites, must become aware of one another

2) Education. Communities need to understand the links between personal habits, active living choices and walkable places.

3) Place-specific/People-specific Data. Communities need an opportunity to consider: “How comfortable and inviting is this place for people who live, work, shop and recreate in the area, or would if they could?”

4) Formation of Alliances. Community organizations involved in planning and local health leadership, such as health departments, need to connect with special interest and neighborhood groups as well as planning and transportation agencies.

5) Lobbying and Fund Raising. New or strengthened alliances then organize to lobby and/or write grant applications. These are the next steps to focus technical awareness on the problem and add political momentum to solving it.

The WPS is an activity for communities to conduct at an early stage in their effort to give greater attention to the walkability, land use, urban design and transportation components of key neighborhood areas. These events should help communities rapidly progress to more technical issues related to a specific problem or project, or more comprehensive issues such as development of a vision statement for more walkable places. The approach has been developed to serve public health and community planning program needs in: 1) local capacity building, 2) assessing environmental conditions, 3) focusing priorities and 4) providing technical assistance. WPS is an established approach that has been well received by government officials, business and community leaders, and non-profits to assess existing conditions for walking and envision design improvements.

Quality of Service

Transportation system quality can be Evaluated by surveying users concerning their views of how well various components meet their needs, their evaluation of attributes such as convenience, comfort, safety and affordability, and descriptions of the problems and barriers they perceive. For example, the 1995 National Personal Transportation Survey includes questions that rate highway, transit, sidewalks, bicycle facilities and air travel on a scale from “excellent” to “poor” (NPTS 1997).

Carreno, Willis and Stradling (2002) developed pedestrian Quality of Service ratings for local roads by surveying employees and shoppers concerning what they consider the performance and importance of 17 attributes. They then categorized the results to identify the low-performance/high-importance features, that is, the most significant problems facing pedestrians, and therefore the best opportunities for improving pedestrian conditions. The box below illustrates an example of their findings. An interesting finding is that some factors that these pedestrians considered of high importance and high dissatisfaction are not generally included when evaluating pedestrian conditions, such as air quality and street cleanliness, while they consider sidewalk crowding and obstacles of relatively minor importance.

Table 17 Pedestrian Quality of Service - Lothian Road (Carreno, Willis and Stradling 2002)

Low Importance

High Importance

Very Dissatisfied

Suitable shopping options

Attractive buildings

Clean air

Street cleanliness

Pavement condition

Personal security

Traffic safety

Less Dissatisfied

Suitable places to stop and rest

Crowded sidewalks

Sidewalk obstacles

Suitable eating and drinking

Inadequate street crossings

Inadequate space

Unsafe street crossings

Low walking speed

Poor public transit access

This table summarizes the results of a pedestrian quality of service survey (Lothian Road, Edinburgh), indicating the level of performance and importance of various roadway conditions.

The UK Transportation Research Laboratory developed a pedestrian environment review process which takes into account the various factors identified in Table 18 (Reid, 2003). TRL developed a computer program to help users quantify these factors and evaluate the walkability of specific geographic areas.

Table 18 Pedestrian Environmental Review System Features (Reid 2003)

Pedestrian Route Review

Pedestrian Link Review

Pedestrian Crossing Review

Directness

Permeability

Road safety

Personal security

Legibility

Rest points

Quality of environment

Effective Width

Dropped kerbs

Gradient

Obstructions

Permeability

Legibility

Lighting

Tactile information

Colour contrast

Personal security

Surface quality

User conflict

Quality of environment

Maintenance

Crossing provision

Deviation from desire line

Performance

Capacity

Delay

Legibility

Legibility for the sensory

impaired

Dropped kerbs

Gradient

Obstructions

Surface quality

Maintenance

Kerry Wood (1999) estimates that bike lanes have the following traffic capacity:

Capacity (cycles/hr) = 1000 + (width (m) - 0.8) x 3300

So, for example, a 1.8 meter width can accommodated approximately 4300 cycles/hr.

The Pedestrian Environmental Quality Index (PEQI) is a quantitative observational survey to assess the bicycle environment on roadways to evaluate what streetscape improvements could be made to promote bicycling in San Francisco (SFDPH, 2008a). The PEQI draws on published research and work from numerous cities to assess how the physical environment impacts on whether people walk in a neighborhood. The PEQI is an observational survey which quantifies street and intersection factors empirically known to affect people’s travel behaviors, and is organized into five categories: traffic, street design, land use, intersections, and safety. Within these categories are 30 indicators that reflect the quality of the built environment for pedestrians and comprise the survey used for data collection. SFDPH aggregates these indicators to create a weighted summary index, which can be reported as an overall index or deconstructed by pedestrian environmental category or even by each indicator.

Table 19 BEQI Indicators by Bicycle Environmental Category (SFDPH 2008a)

Intersection Safety

Vehicle Traffic

Street Design

Perceived Safety

Land Use

Crosswalks

Ladder crosswalk

Countdown signal Signal at intersection

Crossing speed

Crosswalk scramble

No turn on red

Traffic calming features

Additional signs for pedestrians

Number of vehicle lanes

Two-way traffic

Vehicle speed

Traffic volume

Traffic calming features

Width of sidewalk

Sidewalk impediments

Large sidewalk obstructions

Presence of curb

Driveway cuts

Trees

Planters/gardens

Public seating

Presence of a buffer

Illegal graffiti

Litter

Lighting

Construction sites

Abandoned buildings

Public art/historic sites

Restaurant and retail use

Similarly, the Bicycle Environmental Quality Index (BEQI) is a quantitative observational survey to assess the bicycle environment on roadways to evaluate what streetscape improvements could be made to promote bicycling in San Francisco (SFDPH, 2008b). The survey has 23 empirically-based indicators, each of which has been shown to promote or discourage bicycle riding and connectivity to other modes of transport. Several of the indicators have been used in other bicycle indices from different regions in the country, while others are new concepts that have been found significant through other studies regarding healthy bicycle environments. SFDPH identified five main categories which embody important physical environmental factors for bicyclists: vehicle traffic, street design, land use, intersections, and safety. Table 20 details each BEQI indicator under its broader environmental category. These indicators can be aggregated to create the final index (the BEQI), which can be reported as an overall index score, and/or deconstructed by bicycle environmental categories.

Table 20 BEQI Indicators by Bicycle Environmental Category (SFDPH 2008b)

Intersection Safety

Vehicle Traffic

Street Design

Safety/Other

Land Use

Left turn bicycle lane

Dashed intersection bicycle lane

No turn on red signs

Number of vehicle lanes

Vehicle speed

Traffic calming features

Parallel parking adjacent to bicycle lane/route

Traffic volume

Percentage of heavy vehicles

Connectivity of bike lanes

Trees Width of bike lane

Bicycle lane markings Presence of a marked area for bicycle traffic

Pavement type/condition

Driveway cuts Street slope

Bicycle/pedestrian scale lighting

Presence of bicycle lane signs

Line of sight

Bicycle parking

Retail use

The BEQI reveal the relative quality of the biking environment at a street-level scale in select San Francisco neighborhoods. Use of the BEQI can translate environmental variables into a set of provisions for a healthy bicycle environment and a BEQI assessment can inform neighborhood planning and prioritize improvements through the land use plans and environmental assessments.

Network Connectivity

Network Connectivity refers to how efficiently a road or pedestrian network connects destinations. A grid or modified grid road network allows relatively direct connections between destinations, providing a high degree of connectivity. This type of road network design was commonly used until the middle of the 20^th Century, and is promoted by New Urbanism. During the last half of the 20^th Century, many communities were built with a “hierarchical” road network that uses a few major arterials to connect each center or neighborhood. It is common in most suburban communities. This tends to require circuitous travel routes, and so has a low degree of connectivity. This tends to discourage walking and cycling (Dill 2005)

A Connectivity Index evaluates how well a roadway network connects destinations. It is computed by dividing the number of roadway links by the number of roadway nodes. Links are the segments between intersections, node the intersections themselves. Cul-de-sac heads count the same as any other link end point. It can be calculated separately for pedestrian and cycling access, taking into account connections and links for non-motorized travel, such as a path that connects the ends of two cul-de-sacs.

A higher index means that travelers have increased route choice, allowing more direct connections for access between any two locations. According to this index, a simple box is scored a 1.0. A four-square grid scores a 1.33 while a nine-square scores a 1.5. Deadend and cul-de-sac streets reduce the index value. This sort of connectivity is important for non-motorized accessibility. A score of 1.4 is the minimum needed for a walkable community.

Walking Permeability Index

The Walking Permeability Index (WPDI) indicates how directly a pedestrian can reach destinations (Soltani and Allan 2005).

Walking Permeability Distance Index = Actual Walking Distances / Direct Distances

This indicates the connectedness of the pedestrian network. For example, if streets are connected, relatively small, and have good sidewalks, pedestrians can walk directly to destinations, resulting in a low index. If the street network has many unconnected deadends, blocks are large, and lack sidewalks, pedestrians must walk much farther to reach destinations, resulting in a higher index. A WPDI of 1.0 is the best possible rating, indicating that pedestrians can walk directly to a destination. An average WPDI value of 1.5 is considered acceptable for pedestrian accessibility. This index can also be based on travel time to better reflect factors such as delays to pedestrians crossing a street.

Complete Streets

Complete Streets means that roadways are designed to accommodate all modes, including walking and cycling (America Bikes 2004). It involves policies to insure that walking and cycling travel need are taken into account in all appropriate roadway projects, although it can be flexible in how this is done. It can also involve planning and field surveys to identify where barriers exist to non-motorized travel and funding to correct these problems. It often requires new relationships between different levels of government, such as match funding and maintenance agreements between state/provincial transportation agencies and local governments. The Complete Streets concept is promoted by cycling and walking organizations.

Universal Design

Universal Design refers to facility design practices that accommodate the widest range of potential users, including people with disabilities and other special needs. Universal Access is a comprehensive concept, since some special design requirements are not related to disabilities. For example, tall people are not usually considered “disabled,” but their needs should be considered in facility design. Similarly, pedestrians with strollers or pushcarts are not disabled, but still require smooth surfaces and curb cuts.

Modeling Non-motorized Transportation

Transportation planning often uses computerized Models to predict travel demand and evaluate travel conditions. In recent years a number of efforts have been made to better incorporate non-motorized modes into conventional transportation models or to develop specialized models for walking and cycling (Eash 1999; Thorsten, et al. 1999). Several models have recently been developed to evaluate pedestrian access to public transit, including Kittleson & Associates (2013). PBQD 2000 describes data collection and research needs to accurately incorporate non-motorized travel into conventional transportation models. BTS (2000) identifies existing data sources and the additional data required to improve modeling of non-motorized travel.

Rendall, Rose and Janssen (2012) developed the Abley Cycle Route-Choice Metric (ACRM), which accounts for the preferences that cyclists have for certain types of facilities, and therefore the additional distances they will ride to avoid vehicle traffic or hills. Ray Tomalty and Murtaza Haider evaluated how community design factors (land use density and mix, street connectivity, sidewalk supply, street widths, block lengths, etc.) and a subjective walkability index rating (based on residents' evaluation of various factors) affect walking and biking activity, and health outcomes (hypertension and diabetes) in 16 diverse British Columbia neighborhoods. The analysis reveals a statistically significant association between improved walkability and more walking and cycling activity, lower body mass index (BMI), and lower hypertension. Regression analysis indicates that people living in more walkable neighbourhoods are more likely to walk for at least 10 daily minutes and are less likely to be obese than those living in less walkable areas, regardless of age, income or gender. The study also includes case studies which identified policy changes likely to improve health in specific communities.

The Copenhagenize Index evaluates a compares cities’ bikability. A city’s overall rating is the sum of the following criteria rated one to four:

Advocacy: How is the city's (or region/country) advocacy NGO(s) regarded and what level of influence does it have? Rated from no organised advocacy to strong advocacy with political influence.
Bicycle Culture: Has the bicycle reestablished itself as transport among regular citizens or only sub-cultures? Rated from no bicycles on the urban landscape/only sporty cyclists to mainstream acceptance of the bicycle.
Bicycle Facilities: Are there readily accessible bike racks, ramps on stairs, space allocated on trains and buses and well-designed wayfinding, etc? Rated from no bicycle facilities available to widespread and innovative facilities.
Bicycle Infrastructure: How does the city's bicycle infrastructure rate? Rated from no infrastructure/cyclists relegated to using car lanes to high level of safe, separated cycle tracks.
Bike Share Programme: Does the city have a comprehensive and well-used bike-sharing programme? Rated from no bike share programme to comprehensive, high-usage programme.
Gender Split: What percentage of the city's cyclists are male and female? Rated from overwhelming male to an even gender split or more women than men cycling.
Modal Share For Bicycles: What percentage of modal share is made up by cyclists? Rated from under 1% to over 25%.
Modal Share Increase Since 2006: What has the increase in modal share been since 2006 - the year that urban cycling started to kick off? Rated from under 1% to 5%+.
Perception of Safety: Is the perception of safety of the cyclists in the city, reflected in helmet-wearing rates, positive or are cyclists riding scared due to helmet promotion and scare campaigns? Rated from mandatory helmet laws with constant promotion of helmets to low helmet-usage rate.
Politics: What is the political climate regarding urban cycling? Rated from the bicycle being non-existent on a political level to active and passionate political involvement.
Social Acceptance: How do drivers and the community at large regard urban cyclists? Rated from no social acceptance to widespread social acceptance.
Urban Planning: How much emphasis do the city's planners place on bicycle infrastructure - and are they well-informed about international best practice? Rated from car-centric urban planners to planners who think bicycle - and pedestrian - first.
Traffic Calming: What efforts have been made to lower speed limits - for example 30 km/h zones - and generally calm traffic in order to provide greater safety to pedestrians and cyclists? Rated from none at all to extensive traffic-calming measures prioritising cyclists and pedestrians in the traffic hierarchy.

Loudon, Roberts and Kavage (2007) developed the TDM Effectiveness Evaluation Model (TEEM), a tool for evaluating the effect of improving bicycle and pedestrian access to employment sites through physical improvements. The tool is based on an index of bicycle and pedestrian accessibility reflecting the extent of physical infrastructure to accommodate these two modes. Data on commute mode in the Puget Sound region was correlated with the index values to produce a functional relation between the two. The results were used to estimate the change in walk and bicycle commute mode shares that would result from a specified percentage increase in the index values, and estimates of the costs of such improvements.

Stonor, Campos and Smith (2001) evaluate factors that affect urban pedestrian travel in London, UK, using a computer model of “spatial integration” (the level of pedestrian accessibility of urban locations) and other street features. They found that footway accessibility, ground level activity, pedestrian crossing design, traffic signal phasing and time of day are the most important factors. Similarly, Soltani and Allan (2005) evaluate various factors that affect walking and cycling in Adelaide, Australia. They found that various factors including overall walkability, road/path connectedness and land use accessibility all affect the amount of motorized and non-motorized travel that occurs in a neighborhood.

Eash (1999) describes techniques used to modify the Chicago Area Transportation Model to incorporate pedestrian and bicycle travel. Smaller size analysis zones were created and various demographic and transportation system factors that affect non-motorized travel behavior were incorporated into the model. This article should be useful to planners and modelers who might want to incorporate non-motorized travel into a conventional traffic model.

Clark (1997) describes how conventional traffic models used in small cities (Bend and Pendleton Oregon) were modified to account for non-motorized trips. This was used to evaluate pedestrian and bicycle improvements and predict how they could reduce automobile trips. This analysis concluded that pedestrian and bicycle improvements could reduce automobile trips by 4.6% in Bend and 3.8% in Pendelton.

Generic pedestrian and bicycle trip generation data are described in University of North Carolina (1994), Clarke and Tracy (1995), and national surveys such as NPTS and the national Census. Table 21 summarizes factors that affect walking trips which should be given special attention when modeling pedestrian travel.

Table 21 Factors Affecting Pedestrian Travel

Feature	Definition	Indicators
Network continuity	Whether sidewalks and paths exist, and connect throughout an area.	· Portion of streets with non-motorized facilities. · Length of path per capita. · Network connectivity and density (kilometers of sidewalks and paths per square kilometer).
Network quality	Whether sidewalks and paths are properly designed and maintained.	· Sidewalk and path functional width. · Portion of sidewalks and paths that meet current design standards. · Portion of sidewalks and paths in good repair.
Road crossing	Safety and speed of road crossings	· Road crossing widths. · Motor vehicle traffic volumes and speeds. · Average pedestrian crossing time. · Quantity and quality of crosswalks, signals and crossing guards.
Traffic protection	Separation of non-motorized traffic from motorized traffic, particularly high traffic volumes and speeds.	· Distance between traffic lanes and sidewalks or paths. · Presence of physical separators, such as trees and bollards. · Speed control.
Congestion and user conflicts	Whether sidewalks and paths are crowded or experience other conflicts.	· Functional width of sidewalk and paths. · Peak-period density (people per square meter) · Clearance from hazards, such as street furniture and performers within the right-of-way. · Number of reported conflicts among users. · Facility management to minimize user conflicts.
Topography	Presence of steep inclines.	· Portion of sidewalks and paths with steep inclines.
Sense of Security	Perceived threats of accidents, assault, theft or abuse.	· Reported security incidents. · Quality of visibility and lighting.
Wayfinding	Guidance for navigating within the station and to nearby destinations.	· Availability and quality of signs, maps and visitor information services.
Weather protection	User protected from sun and rain.	· Presence of shade trees and awnings.
Cleanliness	Cleanliness of facilities and nearby areas.	· Litter, particularly potentially dangerous objects. · Graffiti on facilities and nearby areas. · Effectiveness of sidewalk and path cleaning programs.
Attractiveness	The attractiveness of the facility, nearby areas and destinations.	· Quality of facility design. · Quality of nearby buildings and landscaping. · Area Livability (environmental and social quality of an area). · Community cohesion (quantity and quality of positive interactions among people in an area). · Number of parks and recreational areas accessible by non-motorized facilities.
Marketing	Effectiveness of efforts to encourage non-motorized transportation.	· Quality of non-motorized education and promotion programs. · Non-motorized transport included in Commute Trip Reduction programs.

Table 22 summarizes factors that affect cycling trips which should be given special attention when modeling bicycle travel.

Table 22 Factors Affecting Bicycle Travel Demand (Based on Levitte 1999)

Factors	Bicycle Travel Impacts
Age	Bicycle use increases into middle age and then decreases. Cyclists tend to have lower average age than non-cyclists.
Gender	Men tend to cycle significantly more than women.
Education	Bicycle use increases slightly with education.
Students	Students are the largest bicycle commuter group. Universities, colleges and schools are major generators of bicycle trips.
Car and License	People who do not have a car available are more likely to cycle.
Having a Drivers License	People who cannot drive are more likely to cycle.
Size of Town	A population of less than 100,000 appears to offer a better environment for cycling, and so may have higher rates of cycling than larger cities.
Employment Status	Higher unemployment is associated with more cycling.
Professional Status	Among employed people, professionals and managers appear more likely to cycle than blue collar and sales workers.
Household Income	Utilitarian cyclists tend to have lower average incomes compared with non-cyclists. Recreational cyclists tend to have higher than average incomes.
Trip Length	Cycling is most common for short (<5 mile) trips.
Parking Fees	Commuters who must pay for parking may be more likely to bicycle.
Facility Conditions	Bicycle facilities (paths and lanes) and roadway conditions considered favorable to cycling tend to increase bicycle travel.
Trip distance	Cycling tends to be used for moderate (<5 km) trips.
Travel costs	Market trends or Transportation Demand Management measures that increase automobile trip costs may induce shifts from driving to bicycling.
Bicycle Parking	Bicycle parking may affect some cycling decisions, particularly the availability of high-security, covered bike storage at worksites.
Community values	Some communities appear to accept and support utilitarian cycling more than others.

Some researchers are using technically-advanced modeling techniques to evaluate and predict pedestrian travel. The STREETS (www.casa.ucl.ac.uk/~david/CUPUM99/SlideShow/Slide1.html) project at the Centre for Applied Spatial Analysis, University College, London (Thorsten, et al, 1999), and the PEDFLOW project at the Transportation Research Institute of Napier University (www.dcs.napier.ac.uk/pedflow) are developing “agent-based” models of pedestrian movement that simulate the behaviour of individuals as they negotiate a virtual walking environment, to aid the design of pedestrian networks and facilities. A combination of observational and interview techniques are used to identify and quantify different movement behaviors (e.g. overtaking, yielding) in response to particular obstructions (e.g. bus stop, approaching person) given a particular set of environmental and personal circumstances.

Managing Non-motorized Facilities

There are often conflicting demands on the use of non-motorized facilities (sidewalks and paths). In particular, conflicts often develop between pedestrians, skaters, cyclists, and powered modes. There are two general ways to address these conflicts:

· Separate modes, for example, by building separate facilities for walking and cycling, and where special facilities do not exist, require skates, scooters and bicycles to use the roadway, or not be used at all.

· Control the use of each mode, by establishing and promoting user behavior guidelines concerning maximum speed and which mode must yield to each other, and where necessary, establishing and enforcing regulations.

In practice, most non-motorized facilities have some degree of shared use (use by a variety of modes). It is infeasible to create separate facilities for every mode everywhere, and even with modal separation conflicts still develop, for example, between walkers, wheelchair users and runners on a sidewalk, and between slow and fast cyclists on paths.

Rather than focusing on specific modes, it tends to be more productive to manage behavior based on performance and priority. For example, although cyclists should generally ride on the road rather than a sidewalk, sidewalk cycling can be appropriate in some situations, for example, when an inexperienced or unskilled cyclist must ride along a busy arterial or bridge with narrow lanes, if the adjacent sidewalk has little foot traffic. Strict enforcement of the no-cycling-on-sidewalks rule forces some children and elderly cyclists to ride in traffic, and can effectively prohibits their cycling on some corridors. As a result, the no-cycling-on-sidewalks rule is often ignored. Similarly, rather than debating whether Powered Human Transporters belong on sidewalks or roadways, it may be more productive to identify under which conditions they belong on sidewalks and how they should behave when using them, and under which conditions they belong on roadways and their appropriate behavior there.

Put a different way, it is not the mode that creates conflicts as much as user behavior. A 12 miles-per-hour (mph) runner does not belong on a crowded sidewalk any more than a cyclist or powered wheelchair going that speed, while a skater or cyclist going 6 mph is better off using a sidewalk, if it is not too crowded, than on a narrow roadway with high speed traffic.

In practice, virtually any non-motorized facility requires some degree of management involving a combination of education and enforcement regarding the safe and considerate sharing between different types of users. Table 23 compares various modes in terms of their performance and priority (based on whether they help provide Basic Mobility or tend to be more optional or recreational uses). Of course, these are general factors that may need to be modified to address the needs of a particular situation.

Table 23 Non-motorized Facility Users Compared

Mode	Speed	Size (Width)	Maneuverability	Priority
Walkers	Low	Narrow	High	High
Walkers with children	Low	Medium to large	Medium to low	High
Walkers with pets	Low	Medium to large	Medium to low	Medium
Human powered wheelchairs	Low	Medium	Medium	High
Motor powered wheelchairs	Medium	Medium	Medium	High
Joggers and runners	Medium to high	Narrow	Medium	Medium
Skates, skateboards and push-scooters	Medium	Narrow	Medium	Low
Powered scooters and electric human transporters (Segway)	Medium	Medium	Medium	Medium
Wagons and pushcarts	Low	Medium to large	Medium to high	Medium
Human powered bicycle	Medium to high	Medium to large	Medium to low	Medium
Motorized bicycle	High	Large	Low	Low
Equestrians	Medium to high	Large	Low	Low

This table compares various modes that use pedestrian facilities.

This approach is intended to help decision-makers develop appropriate guidelines and regulations to manage the use of non-motorized facilities. These guidelines can reflect the physical performance and value of each mode, identifying when and were a particular mode may be used, their maximum speeds, and which mode should yield to another. Below are examples that may be adopted by a particular community or agency:

· Lower-speed, smaller modes should be given priority over higher-speed, larger modes.

· Higher-priority modes should yield to lower-priority modes. For example, modes that help provide Basic Mobility (walking and wheelchair users) should have priority over more recreational modes (such as skateboards).

· Maximum speeds should be established for each mode, based on the physical design of the facility (i.e., some facilities may only accommodate 10 mph cycling, while others can accommodate 15 mph cycling. Maximum speeds should decline as a pedestrian facility becomes more crowded or narrower.

· If facilities cannot accommodate all potential modes, higher-priority modes should have priority, and lower-priority modes should be required to use roadways. For example, cycling, skating and equestrians may be allowed on pedestrian facilities at uncrowded times and locations, but not at busy times and locations.

· Cyclists, skaters and motorized modes should reduce their speed when using mixed use paths (6-12 mph maximum, depending on conditions) and yield to non-motorized modes. For higher-speeds, runners, cyclists and powered modes should use roadways.

· Owners should be responsible for the behavior of their pets.

· Special efforts should be made to accommodate a wide range of users (including cyclists, skaters and runners) where there are no suitable alternative routes (e.g., roadways are unsuitable for such modes).

· All modes should use extra caution when passing children and pets.

· Special consideration may be given to equestrians, since they horses are easily frightened and difficult to maneuver.

Valuing and Prioritizing Improvements

Transportation planning involves countless decisions concerning the allocation of public resources and the management of public facilities. These tradeoffs determine the convenience, speed and safety of different modes, and so effectively Prioritize transportation activities and the allocation of costs and benefits.

There are four factors to consider when evaluating barriers and gaps in pedestrian and cycling networks, and when prioritizing improvements:

1. Level of demand. How many people would use a facility if it were improved. In general, this increases around higher density areas, such as business districts and higher-density residential areas, and around attractions, such as schools and parks.

2. Degree of barrier. This can range from minor difficulties (such as requiring pedestrians to use a longer route than if a proposed improvement is made) to a total barrier to walking and bicycling. The degree of barrier also depends on who is traveling, and under what conditions. People with physical disabilities are more vulnerable to such barriers.

3. Potential benefits. This refers to the benefits that could result from increased walking and cycling on that corridor. For example, improvements that encourage non-motorized travel to substitute for driving may provide more value to a community than improvements used primarily for recreational cycling and walking.

4. Cost and ease of improvement. This includes the incremental financial costs of the project, and any increase in future maintenance costs.

The Bicyclepedia (www.bicyclinginfo.org/bikecost) is a bicycle facility benefit/cost analysis tool available free on the Internet. The New Zealand Transport Agency Economic Evaluation Manual includes specific procedures for evaluating walking and pedestrian improvements. It applies a benefit factor of $2.70/km to new or safer pedestrian trips, and $1.45/km for new or safer cycling trips (NZTA 2010, Vol. 2, p. 8-11).

Alveano-Aguerrebere, et al. (2018) evaluated potential cycling improvements in developing country (Mexico) conditions. They find that potential users prefer separated bike paths with solid barriers that motorists cannot cross because they are associated with the greatest safety from crashes, lowering crime, and contributing to economic development. Shared use recreational paths were associated with lowering the probability of car/bike crashes but lacked the potential to deter crime and foster the local economy. Joint bus and bike lanes were considered to provide less safety because Mexican bus drivers tend to offer little courtesy to bicyclists.

Patterson and Fadum (2013) developed a Cycle Zone Analysis Tool which integrates data on cycling demand (neighborhood population and demographics), roadway conditions (Bikeway Quality Index and Intersection Quality Index), path and street connectivity, and topography (hills) to rate the bikeability of various areas in a city.

Litman (2000 and 2009) and Ker (2001) describe estimates of the monetized benefits of travel shifted from driving to non-motorized modes. Guo and Gandavarapu (2010) identify various geographic and demographic factors that affect non-motorized travel activity, and evaluate the cost effectiveness of neighborhood sidewalk construction based on health and air pollution reduction benefits.

Table 24 Value of 1,000 km Shifted From Driving to Walking (Ker 2001)

Item	Current Year	10 years	30 Years
Vehicle operating cost savings	$113	819	1,446
Improved health	84	607	1,071
Crash risk (from increased walking)	-95	-687	-1,212
Crash risk (from reduced driving)	34	246	435
Reduced air pollution	20	145	256
Reduced greenhouse gas emissions	20	145	256
Reduced traffic noise	3	22	38
Reduced water pollution	2	11	19
Total Benefits (Net Present Value)	$181	1,318	2,339

This table illustrates the monetized benefits to society of 1,000 kilometers shifted from driving to walking, in 2001 Australian dollars, using a 7% annual discount rate. This can be used to estimate the value of an investment or policy that results in such a shift.

Schwartz, et al., (1999) is a comprehensive guidebook that describes and compares various techniques that can be used to evaluate and prioritize non-motorized projects. It provides an overview of each method, including pros and cons, ease of use, data requirements, sensitivity to design factors, typical applications, and whether it is widely used.

The city of Portland (Portland Office of Transportation, 1998) uses two factors to prioritize pedestrian improvements. The “Pedestrian Potential Index” measures the potential demand for pedestrian travel based on the areas PEF (described above), proximity to activity centers (such as schools, housing [especially senior housing] parks, transit, neighborhood shops), and policy factors, such as whether improvements to the pedestrian environment on that street are part of the regional strategic plan. The “Deficiency Index” measures how critically pedestrian improvements are needed. The highest priority for pedestrian improvements are projects which rank high on both the Potential and Deficiency indices. The same method could be used to prioritize cycling projects.

Portland Office of Transportation (1998) describes how pedestrian improvements can be prioritized using GIS (Geographic Information System) modeling to match information on pedestrian travel demand (where people want to walk) with information on pedestrian conditions (where there are barriers to walking).

In a number of ways, current transportation planning and project evaluation practices tend to be biased in favor of automobile transportation improvements at the expense of non-motorized improvement (Goodwin 2004; Jopson, Page and Menaz 2007). The Distillate (www.distillate.ac.uk) program is now working to develop better evaluation methods. More comprehensive economic analysis tends to increase support for non-motorized transport. For example, a cost-benefit analyses (CBAs) of walking- and cycling facilities in Norwegian cities, taking account health benefits, reduced air-pollution and noise from road traffic, and reduced parking costs when travel shifts from automobile to cycling and walking, concludes that benefits of improved non-motorized travel facilities are least 4-5 times their costs (Sælensminde, 2004).

Where Sidewalks Are Unnecessary

There are some conditions where non-motorized facilities in general and sidewalks in particular are a relatively low priority. These include:

· Roads with virtually no demand for walking. However, the absence of pedestrians does not prove that there is no demand – there may be latent demand, that is, people would like to walk but do not because access is so poor or conditions are so unpleasant. In fact, there are few roadways that really have no pedestrian demand.

· Roads where automobile traffic is very light and slow. On some streets pedestrians can comfortably use the roadway because there are few motor vehicles (i.e., less than 10 motor vehicles per hour), and motorists drive slowly and yield to pedestrians.

Non-motorized Planning Guides

Several publications provide general guidance for Non-motorized Transportation Planning. Litman, et al. (2000) is a comprehensive guide to non-motorized planning. It includes non-motorized planning and modeling techniques, design and maintenance standards, education, law enforcement and other related issues. It provides an overview of each issue and detailed references for more detail on individual subjects.

Wit and Humor

Why the Chicken Crossed the Road, by Dr. Seuss

Did the chicken cross the road?

Did he cross it with a toad?

Yes, the chicken crossed the road!

But why it crossed, I’ve not been told.

Examples and Case Studies

Active Transportation Benefit/Cost Calculator

Transportation programs and projects are often evaluated using benefit-cost analysis, to ensure that their total benefits exceed their total costs, and to compare and prioritize potential projects. However, existing transportation benefit-cost analysis tools are inadequate for evaluating active transport. To fill this gap the California Department of Transportation developed the Active Transportation Benefit-Cost Calculator for evaluating pedestrian and bicycle projects (Cooper and Danziger 2016; www.dot.ca.gov/hq/tpp/offices/eab/atp.html).

The Tool incorporates the following impacts:

Project costs
Changes in travel activity
Changes in crashes
User benefits

Physical fitness and health benefits from more active transport
Various savings from projects that reduce motor vehicle travel
Land use benefits from projects that encourage more compact development

The current version of the calculator incorporates some omissions and biases that will tend to underestimate active transportation benefits. For example, it excludes some often-large benefit categories, such as reduced chauffeuring burdens and parking facility cost savings, based on the assumption that they are difficult to calculate, and it applies a conventional travel time cost values that are excessive for travellers who enjoy walking and cycling. However, the model can be adjusted to account for these factors. Despite these weaknesses, this Tool is a major contribution to active transportation economic evaluation.

Active Transportation for America (www.railstotrails.org/ATFA)

This report quantifies the transportation, energy, climate, public health, and economic benefits of bicycling and walking. It estimates that bicycling and walking currently account for 10% of all trips in America, and could play a much larger role if governments invested more in safe and convenient places to bicycle and walk. “The report shows that modest increases in individuals bicycling and walking could lead to an annual reduction of 70 billion miles of driving, and more substantial increases could avoid 200 billion miles each year,” says Thomas Gotschi, research director for Rails-to-Trails Conservancy. This could cut oil dependence and climate pollution from passenger vehicles by 3 to 8 percent, out-performing the historic contribution of other prominent solutions such as gas-electric hybrid vehicles.

Benchmarking Report (www.peoplepoweredmovement.org/benchmarking)

The Alliance for Biking & Walking’s Benchmarking Reports is an on-going effort to collect and analyze data on bicycling and walking in all 50 states and the largest U.S. cities.

It investigated:

Bicycling and walking levels and demographics
Bicycle and pedestrian fatalities
Bicycle and pedestrian policies and provisions
Funding for bicycle and pedestrian projects
Bicycle and pedestrian staffing levels
Written policies on bicycling and walking
Bicycle infrastructure including bike lanes, paths, signed bike routes, and bicycle parking
Bike-transit integration including presence of bike racks on buses, bike parking at transit stops, and hours per week that bicycles are allowed on trains
Public health indicators including levels of obesity, physical activity, diabetes, and high blood pressure.

It found:

A positive relationship between the built environment and levels of biking and walking.
Where levels of biking and walking are higher, bicycle and pedestrian safety is greater.
Cities with strong advocacy organizations generally have high levels of biking and walking.
Higher levels of biking and walking coincide with lower levels of obesity, high blood pressure and diabetes and higher levels of adults meeting recommended levels of daily physical activity.
Data revealed that while some cities and states lead others as models for bicycle and pedestrian policies and provisions, all states and cities have a need for improvement.

Non-Motorized Transportation (www.trb.org/main/blurbs/171138.aspx)

The report, Estimating Bicycling and Walking for Planning and Project Development: A Guidebook (Kuzmyak, et al. 2014) describes practical methods and tools for estimating bicycling and walking demand as part of regional-, corridor-, or project-level analyses. These methods are sensitive to key planning factors, including bicycle and pedestrian infrastructure, land use and urban design, topography, and sociodemographic characteristics. The planning tools presented include some entirely new methods as well as some existing methods found to have useful properties for particular applications. These tools take advantage of existing data and the capabilities presented in GIS methods to create realistic measures of accessibility which are a critical determinant of bicycle, pedestrian, and even transit mode choice. This information should be of considerable value to transportation practitioners either directly interested in forecasting bicycling or walking activity levels or accounting for the impact of bicycle or pedestrian activity in support of broader transportation and land use planning issues

Bicycle Improvement Benefit/Cost Analysis (Gotschi 2011)

This study assessed how costs of Portland’s past and planned investments in bicycling relate to health and other benefits. Bicycle facility costs are compared with 2 types of monetized health benefits: health care cost savings and value of statistical life savings. Levels of bicycling are estimated using past trends, future mode share goals, and a traffic demand model. This analysis indicates that by 2040, investments in the range of $138 to $605 million will result in health care cost savings of $388 to $594 million, fuel savings of $143 to $218 million, and savings in value of statistical lives of $7 to $12 billion. The benefit-cost ratios for health care and fuel savings are between 3.8:1 and 1.2:1, and an order of magnitude larger when value of statistical lives is used. This indicates that such efforts are cost-effective, even when only a limited selection of benefits is considered.

Bicycle Lane Impacts On Retail (Sztabinski 2009)

A study that examined the impacts of proposed bike lanes on retailers along Bloor Street in Toronto found that:

Only 10% of patrons drive to stores, the rest arrive by walking, cycling or public transit.
Even during peak periods no more than about 80% of metered parking spaces on the street are occupied.
Patrons arriving by foot and bicycle visit the most often and spend the most money per month.
More merchants who believe that a bike lane or widened sidewalk would increase business than merchants who think those changes would reduce business.
Patrons would prefer a bike lane to widened sidewalks at a ratio of almost four to one.
The reduction in on‐street parking supply from a bike lane or widened sidewalk could be
accommodated in the area’s off‐street municipal parking lots.

This analysis indicates that in many situations, expanding sidewalks and converting bicycle and transit lanes, or wider sidewalks, could support local economic development.

Queensland Active Transport Benefits (SKM and PWC 2011)

A 2011 Queensland Australia government-sponsored study estimates that an average bicycle commuter provides $14.30 in economic benefits and a pedestrian commuter provides $8.48 worth of benefits, including:

· Decongestion (20.7 cents per kilometre walked or cycled).

· Direct health benefits (up to 168.0 cents per kilometre).

· User vehicle operating cost savings (35.0 cents per kilometre).

· Road and parking infrastructure savings (6.8 cents per kilometre).

· Environment (5.9 cents per kilometre).

Pedestrian Quality Needs (www.walkeurope.org)

The Pedestrians’ Quality Needs Project (PQN) is an European research project to provide knowledge of pedestrians’ quality needs and how those needs relate to structural and functional interventions, policy making and regulation to support walking conditions other involved countries. It involves researchers from 25 countries, and will run from 2006 to 2010. It is producing reports on walking conditions in each country and resources to provide practical guidance for pedestrian planning.

PacScore Local Accessibility Indicator (Dock, Greenberg and Yamarone 2012)

The city of Pasadena, California developed the PacScore metric which evaluates local transport system performance based on accessibility, sustainability, livability and user experience. It uses geographic information systems to quantify walkability (the number of destinations accessible within a quarter-mile walk), multi-modal level of service indicators (the convenience and speed of walking, cycling, public transport and automobile travel), and per capita vehicle-travel.

High Technology and the Humble Pedestrian are About to Meet.

By Neal Peirce, Washington Post Writers Group

DUBLIN, Ireland - Through an ingenious piece of software called Amble Time, being developed at Media Lab Europe here, digital maps of city streets and pathways can be wed with information about an individual’s walking speed.

Say you’re at Dublin’s train station, your walking speed is 3 mph, and you wonder which attractions you can reach in a 20-minute walk. The software shows what is within your reach.

As it’s perfected, Amble Time will be combined with the global positioning system and available on hand-held computers. A pedestrian’s location, pauses and en-route variations will be noted automatically. The pedestrian will also be able to check transit schedules, hours of movies or museums, and restaurant locations.

The pedestrian focus of this project at the new Dublin Media Lab -- financed by the Irish government and affiliated with the Media Lab at the Massachusetts Institute of Technology -- isn’t as “off the map” of global concerns as you’d think. Focusing on pedestrians’ needs is rising as a concern across continents.

Consider Indianapolis, a classic auto-dominated city known for broad streets, weak public transit and a roaring Speedway. Indy is seriously considering a new pathway system that would close traffic lanes on some major arteries in order to remake the downtown into a radically more attractive place for walkers and bikers.

The plan is being considered by the area’s Metropolitan Planning Organization, which makes decisions on area transportation spending. It would create a cultural trail linking such attractions as downtown shopping and monuments, the White River State Park, Canal Walk, and the arts and entertainment offerings in the Fountain Square area.

Why would Indianapolis consider a walkway system? The official line is “economic development,” but the reason is really broader. It’s character -- to create a more alluring place where people (businesses included) will want to be.

Talk to Enrique Penalosa. He’s a Duke University graduate who’s former mayor of Bogota and a potential presidential candidate of Colombia. You hear an even deeper justification for creating cities for walking.

Penalosa sees a global battle to reclaim our cities from the automobile, which expanded its hold through the 20th century, occupying boulevards and streets and public spaces, subjugating the cities’ people to its voracious demands.

Penalosa delights in telling how, on becoming mayor of Bogota in the late ‘90s, he made a priority of creating walkable spaces: pedestrian streets, sidewalks, greenways, bike paths and parks in neighborhoods and on a metro region scale.

“I was almost impeached,” Penalosa confesses, “for getting cars off sidewalks which car-owning upper classes had illegally appropriated for parking.” There were lawsuits to block greenways that would connect lower-income neighborhoods to upper-income sectors.

Many objected to the yearly car-free day that Penalosa instituted, in which everyone was obliged to get to work or school by bus, bike, taxi or foot. Deep muttering was heard when he doubled the street miles covered by the city’s traditional “Ciclovia” -- seven hours each Sunday when the streets are available for walking, biking, jogging and socializing, but not motorized vehicles.

Why did Penalosa do all this? Because, he says, parks, pathways and open spaces reclaim the city for its people, which restores their dignity. The mission for our times, he argues, should be “a city more for children than for motor vehicles.”

There’s an undertone of Third World class struggle in Penalosa’s argument that’s not entirely appropriate to North America, where the middle class and even a significant segment of the poor have autos.

But there’s a universal, humanistic side to his appeal: “God made us walking animals -- pedestrians. As a fish needs to swim, a bird to fly, a deer to run, we need to walk, not in order to survive, but to be happy.”

Cities, Penalosa says, are where all classes of humanity brush shoulders -- not in their vehicles, which separate them and indicate class status, but on sidewalks and in parks, those shared civic spaces where all are equal.

No one is suggesting that automobiles -- a colossus of the global economy and devices of incredible convenience -- will go away. But it’s hard to miss the whisper of a new era in which pedestrians start to reclaim their place in world cities.

More Cycling Facilities Increase Cycling and Bicycle Safety

A study by the National Association of City Transportation Officials, Equitable Bike Share Means Building Better Places for People to Ride evaluated the relationships between bicycle facility development, cycling activity and bicycle crash rates. It found that:

When cities expand their bike networks, more people bicycle. Studies from North American cities indicate that adding protected bike lanes increases bike ridership on those streets by 21% to 171%.
Riding a bike is getting safer as cities build better bike lane networks. In five of the seven U.S. cities NACTO surveyed, the absolute number of bicyclists killed or severely injured declined from 2007 to 2014, even as bike ridership rates increased. Additionally, even in the cities where the absolute number of bicyclists killed or severely injured increased over the time period, that rate is rising at a slower pace than the increase in bicycling itself. This decline in risk comes at the same time as bike ridership rates in the cities surveyed have more than doubled. All seven cities have invested in high-comfort bike facilities.
Gains in bike safety are especially important for low-income riders and riders of color. 49% of the people who bike to work earn less than $25,000 per year, and Black and Hispanic bicyclists have a fatality rate 30% and 23% higher than white bicyclists, respectively. Building extensive protected bike lane networks benefits those who are most at risk.
Most people are “interested but concerned” about biking and would bike with higher-comfort facilities. 60% of the total population are “interested but concerned” about biking. Of those, 80% would be willing to ride on streets with a separated or protected bike lane. In particular, recent national research suggests that that people of color are more likely than white Americans to say that adding protected bike lanes would make them ride more.
Bike share systems should be matched with extensive protected bike lane networks to encourage ridership and increase safety. People on bike share bikes make up a disproportionate number of the riders on protected lanes, and stations adjacent to bike lanes are busier than ones that are not. For bike share to be successful, people need to feel comfortable riding.
The risk of a bicyclist being struck by a motorist declines as the number of people biking increases. Appropriately scaled bike share systems can dramatically increase the total number of people on bikes in a city and help build political momentum for bike lanes.
Mandatory adult helmet laws reduce bike ridership and don’t increase safety. Mandatory adult helmet laws have reduced bike ridership in Sydney, and hampered bike share ridership efforts in Melbourne and Seattle. In addition to evidence that mandatory adult helmet laws do not increase overall bike safety, reports from across the U.S. suggest that these laws are disproportionately enforced against people of color, further discouraging them from riding.

Tools for Counting Non-Motorized Traffic (http://fhwatmgupdate.camsys.com/images/TMG_Ch4_aug20.pdf)

A major challenge for non-motorized planning is to obtain data on walking and cycling activity. The “Traffic Monitoring For Non-Motorized Traffic” chapter of the US Federal Highway Administration’s Traffic Monitoring Guide (http://fhwatmgupdate.camsys.com/images/TMG_Ch4_aug20.pdf) provides practical information on ways to count the number of pedestrians and cyclists using a facility.

HEAT Model (http://euro.who.int/transport/policy/20081219_1)

The Health Economic Assessment Tool (HEAT) for cycling is a science-based computer model that calculates the human health benefits that result from increased cycling activity (Kahlmeier, et al. 2010). It allows user to model the impact of different levels of cycling, and attach a value to the estimated level of cycling when the new infrastructure is in place. This can be compared to the costs to produce a benefit/cost ratio, or as an input into a more comprehensive cost benefit analysis. It can be applied to a specific project that increases cycling on a facility or corridor, or to a set of policies and projects that increase cycling activity in an area. For instance, to estimate the mortality benefits from achieving national targets to increase cycling or to illustrate potential cost consequences to be expected in case of a decline of the current levels of cycling.

Active Transport Performance Indicators (Semler, et al. 2016)

The Guidebook for Developing Pedestrian and Bicycle Performance Measures is intended to help communities develop performance measures that can fully integrate pedestrian and bicycle planning in ongoing performance management activities. It highlights a broad range of ways that walking and bicycling investments, activity, and impacts can be measured and documents how these measures relate to goals identified in a community’s planning process. It discusses how the measures can be tracked and what data are required, while also identifying examples of communities that are currently using the respective measures in their planning process. This report highlights resources for developing measures to facilitate high quality performance based planning.

Regular Cycling Improves Fitness (Cooper, et al. 2006)

The Copenhagen Center for Prospective Population Studies found a substantial decrease in fatality rates among who spent 3 hours per week commuting to work by bicycle compared to those who did not commute by bicycle, taking into account other risk factors such as diet and leisure activity. Another study of 1,919 students at 25 schools in Odense, Denmark were tested to assess their cardiorespiratory fitness. The results indicate that children and adolescents who cycled to school were significantly more fit than those who walked or traveled by motorized transport and were nearly five times as likely to be in the top quartile of fitness. These results suggest that regular cycling is particularly effective at improving fitness and health.

Sustainable Transportation In The Netherlands (http://wiki.coe.neu.edu/groups/nl2011transpo)

This wiki (open-source encyclopedia) was created by participants of the Sustainable Transportation: European Perspectives field studies program. It includes detailed analysis of pedestrian and bicycle conditions in various cities that illustrate outstanding examples of bicycle planning.

I. Bicycling Facilities in Holland

What kind of bicycle route facilities does one find for riding in Holland? Cycle tracks, bike lanes, bike boulevards, intersection treatments, network analysis, and more. Click here.

II. Sustainable Transportation in Delft

The city of Delft offers many good lessons about designing cities for sustainable transportation. Canals, trams, trains so fast and frequent that they almost function like a metro, large pedestrianized areas, a dense bicycling network,parking policy, land use policies that strengthen the city's shopping core, roads that don't create safety barriers for bikes and pedestrians, road diets, planning mistakes, and more. Click here.

III. Designing Suburbs for Sustainable Transportation

Sustainable urban transportation stems from a combination of land use policy (e.g., housing density, smart locations for employment centers and shopping centers, size and distribution of schools) and transportation policy (providing convenient and safe means for traveling by foot, by bike, by transit). In 1960, Houten (NL), Pijnacker (NL), and Mansfield (MA) had small centers but were otherwise rural; all three are now suburbs with population 25,000 to 50,000. What lessons in planning for sustainable transportation do they offer?

A. Sustainable Transportation in Houten, The Netherlands

Houten is a "new town" suburb of Utrecht, conceived in the late 1970's with a unique traffic circulation plan that makes it the largest slow-traffic cell known in a Western city. It is a unique plan, preeminently bicycle-oriented and family-oriented, recognized worldwide as a model but never duplicated -- until a large south Houten extension built around 2005 showed that it could be done again. Click here.

B. Sustainable Transportation in Pijnacker, The Netherlands

Learn about Dutch practices regarding zoning and urban expansion, and about how Pijnacker's expansion projects in the period 1998-2018 applies many sustainable transportation features including woonerfs, 30-km/h zones, roundabouts, a strong distinction between "through-traffic" roads and "local access" roads, and a traffic circulation plan that confines motor traffic but provides a dense mesh for bicycle traffic. Click here.

C. Sustainable Transportation in Mansfield, MA

Blessed with a railroad station and a sizable center developed around pre-war industry, Mansfield also evidences the pressures of highway-oriented development and suburban sprawl. Learn about its efforts to promote sustainable transportation within the American legal and economic context. Click here.

D. Comparing Other Communities to the Houten Model

Houten's cell model is well known in Dutch urban planning circles. While no other town has completely duplicated it, many towns and urban expansion areas have adopted aspects of this model. This chapter compares some Dutch and American communities to the Houten model. Click here.

Cycling Program Benefit Analysis

Cavill, Cope and Kennedy (2009) estimated that an integrated program to encourage walking in British towns provided a benefit/cost ratio of 2.59 (each £ spent on the program provided £2.59 worth of benefits) from reduced mortality. Benefit/cost ratios of this magnitude are classed as ‘high.’ Including other benefits (such as morbidity; absenteeism; congestion; pollution) would be likely to increase this value. The Department for Transport found even higher economic returns (DfT 2008).

Walkability, Highway Access Important To Home Buyers (www.nahb.com/news/smartsurvey2002.htm)

A 2002 survey conducted by the National Association of Home Builders and the National Association of Realtors indicates that walkability is an important homebuyer preference. While a desire for a larger home was the top choice (64%), 27% said they wished they could walk to more places from home; 23% said they wished their home was closer to work and 17% wanted to be closer to shopping and restaurants.

According to Gary Garczynski, president of NAHB, “This survey demonstrates that home buyers are quite conscious of the tradeoffs they make when buying a home.” “They are willing to live further from the city in order to have a larger home, and the quality of the community is more important then the length of the commute. A better understanding of these tradeoffs enables us to develop planning and growth policies that take into account home buyers’ preferences.”

When asked about the importance of 18 community amenities, the highest ranking features were (with percent ranking as important or very important): highway access, 44 percent; jogging/bike trails, 36 percent; sidewalks, 28 percent; parks, 26 percent; playgrounds, 21 percent, and shops within walking area, 19 percent.

Summing up the survey, Garczynski said “The survey responses suggest a vision of smart growth that home buyers are prepared to embrace. “A majority of consumers want single-family detached homes in a pedestrian-friendly community that has shopping within walking distance. They want a mix of open space, including parks, recreational facilities, playgrounds, farms, nature preserves and undeveloped areas. They want traffic minimized on neighborhood streets. To the extent that we - builders, developers, planners, elected officials - can create high quality, walkable, mixed-use communities, we will deliver a version of smart growth that is more likely to be accepted in the marketplace.”

Is My Area Walkable? Some Questions To Help You Assess The Walkability Of A Locality And How It Can Be Improved.

Anne Matan, Curtin University Sustainability Policy (CUSP) Institute, Australia.

Published in Salter, Dhar and Newman (2011)

Use/Network

1. What is the volume of pedestrian traffic on this street? (pedestrian counts)

2. Who are the people using this street? Do they have special walking needs given their age or disability?

3. What is the pedestrian density of particular footpaths (numbers of pedestrians per metre width of footpath per minute)?

4. What are the main pedestrian routes in the area (day time and night time)?

5. What types of pedestrian facilities are in the area (dirt paths, paved footpaths/sidewalks, shared streets, pedestrian only streets, plazas, squares)?

6. What is the length and area of these pedestrian facilities?

7. What are the main arrival and exit points to the area? Are they connected via walkways?

8. How easy is it to walk through the area? (Do test walks to establish this.)

9. How adequate are footpaths/sidewalks in the area?(Some possible problems: no sidewalks, discontinuous, too narrow)

10. What proportion of streets have footpaths/sidewalks?

11. Are the footpaths/sidewalks complete on both sides of streets?

12. Is the footpath/sidewalk provision satisfactory in both major and smaller streets?

13. Are footpaths wide enough to cater for the number of people who walk on them?

14. What are the footpaths/sidewalks made from? (asphalt, concrete, paving bricks, flagstones, dirt, gravel, etc)

15. Are the sidewalks well maintained? (free from cracks, holes, rubbish, etc)

16. Are the block lengths short? (If they are long there may need to be walkways through the block.)

17. Does the pedestrian network connect major areas/destinations in the city?

18. Does the pedestrian network connect to primary destinations such as schools, hospitals, transit stations?

19. Is the pedestrian network itself well connected (with, for example, few pedestrian cul-de-sacs)?

Barriers

1. Is the area accessible to those with disabilities? Are there ramps instead of steps where possible?

2. Are there obstacles on the footpaths (for example, street trade, shanty dwellings, piles of rubbish, parked cars, animals, road or building construction materials, or a large number of poles and signs)?

3. Are there buffers between the road and the footpath, such as fences, bollards, trees, hedges, parked cars and landscaping? (Buffers have advantages and disadvantages, but they can screen walkways from traffic and prevent parking on the walkways.)

4. Are there many small interruptions to the pedestrian networks (e.g., minor road crossings, parking lot crossings, driveway crossings)?

5. Are there other major barriers to walking in the area (major roads, train tracks, rivers, hills, gated land uses, etc)?

6. Does the slope of the area make it hard to walk?

Intersections

1. How convenient is it to cross the street? Where are the pedestrian crossings?

2. What type of traffic intersections are used?

3. Are pedestrians given priority at intersections?

4. What are the crossing aides used at traffic intersections (pavement markings, different road surface or paving, signs, traffic lights, median traffic islands, curb bulbouts, underpasses, overpasses, etc.)?

5. Is crossing made easier either by curb cuts or road raising?

6. How safe is it to cross the street (at designated pedestrian crossings)?

7. Do drivers obey road laws and traffic signals?

8. Are pedestrian crossings clearly marked?

9. Do traffic signals indicate how long you need to wait before crossing, and how much remaining time you have to complete the crossing?

10. Do you need to press a button for a pedestrian signal to permit you to cross?

11. Are there any mid-block crossings? Are these adequate?

Public Transport Connection

1. Is the area connected to public transport? Where are the public transport nodes?

2. Are the public transport waiting areas of high quality (weather protection, information, signage, seating, waste receptacles. etc)?

Land use

1. What are the primary land uses of the area? (This will suggest the numbers of pedestrians at different times of the day.)

2. What are the primary destinations (industrial, commercial, governmental, recreational, community) in the area?

3. What is the population of residents and workers in the area?

Enjoyment

1. What are the main public areas (square, parks, plazas, etc)? Are they public (open to everyone) or private (limited access, controlled use)?

2. What is the quality of the public spaces (comfort, appearance, maintenance, possibilities for use)?

3. How many people are using these spaces? How are they using this space? (can be assessed through stationary activity counts or behavioural mapping)

4. Are there any spaces for children/elderly/youth within the city?

5. Does the area allow for physical activity, play, interaction and/or entertainment?

6. Are there any identifying features in the area (monuments, land marks, neighbourhood character)?

7. Is there any indication that one is entering a special district or area? (It’s good to have the neighbourhood character indicated in some way along the walkway.)

8. Are the walking areas interesting?

9. Are there interesting views?

10. Are there temporary activities in the area (markets, festivals, buskers, street performers, etc)?

11. Does the area allow for resting, for meeting others, for social interaction?

12. Is there adequate greening in the area (plants, trees, etc)?

13. Is the area of a high visual quality (pavements, facades, art, etc)?

Streetscapes

1. Where buildings meet the street, is it clear what is private and what is public space?

2. Are the dimensions of the buildings lining the footpaths at human scale?

3. Are the facades of the buildings lining the street transparent/active (i.e., do the buildings having many doors and windows opening onto the street, ‘soft edges’, with many niches, detailed facades)?

Infrastructure

1. What is the amount of seating available?

2. Is the seating in the right place (with regard to views, comfort and protection from climatic conditions, located at the edge of spaces)? Does the seating maximise the natural advantages of the area?

3. Are the seating arrangements appropriate (can you talk to friends)?

4. What is the quality of the seating?

5. Are there places to stand? To lean against? Attractive edges?229

6. Are waiting areas adequate, providing comfort and protection to pedestrians waiting for transit or to cross the street?

7. Are there enough rubbish bins?

8. Is there any public art?

9. Are there water fountains?

10. Are there wayfinding devices?

11. Are there public toilets?

Comfort

1. Is there adequate protection from the sun, rain and wind?

2. Is there adequate protection from negative aspects of vehicle traffic (pollution, noise etc)?

3. Are the ambient noise levels low and comfortable?

4. Do the sitelines allow you to see where you are going?

5. Is the area well maintained (footpaths, buildings lining the sidewalks, etc)?

6. Is the area clean (free from rubbish, broken glass, inappropriate graffiti)?

Safety

1. Is the area lively and active?

2. Is there street life?

3. Is there passive surveillance of the area? In other words, are there people around to watch out for each other? (This is especially important when it comes to night-time usage.)

4. Is the area safe? (both perceived and real)

5. Is the lighting from street lights and buildings adequate at night time?

6. Are there signs of other people at night time?

7. Are there night time uses of the area?

8. Is there a mix of land uses in the area?

9. Are there many small land uses?

10. Are the facades of buildings ‘closed’ at night?

11. Is there adequate visibility between modes of transport?

12. Is there protection from vehicle traffic?

Vehicle traffic

1. What is the traffic volume of the street? Does it make it hard/unpleasant for walking?

2. Is there street parking (on/off street)

3. What is the speed limit of the street? Does this make it hard/unpleasant for walking?

4. Are there any traffic calming or traffic control devices in the area?

5. How many lanes of traffic are there?

6. What are the traffic control devices used (traffic lights, stop signs, roundabouts, speed bumps, etc)?

Perception of the area

1. Is the area perceived as safe?

2. Is the area perceived as pleasant?

References And Resources For More Information

AARP (2005), Livable Communities: An Evaluation Guide, AARP Public Policy Institute (http://assets.aarp.org).

AASHTO (2004), Guide for the Planning, Design, and Operation of Pedestrian Facilities, American Association of State Highway and Transportation Officials (www.aashto.org); at www.walkinginfo.org/library/details.cfm?id=2067.

ADUPC (2009), Abu Dhabi Urban Street Design Manual, Abu Dhabi Urban Planning Council (www.upc.gov.ae/en/Home.aspx); at www.upc.gov.ae/guidelines/urban-street-design-manual.aspx?lang=en-US.

ADUPC (2013), Abu Dhabi Public Realm Design Manual, Abu Dhabi Urban Planning Council (www.upc.gov.ae/en/Home.aspx); at www.upc.gov.ae/prdm/index.asp.

ABW (2014-2016), Bicycling and Walking in the U.S.: Benchmarking Reports, Alliance for Biking & Walking, (www.peoplepoweredmovement.org); at www.peoplepoweredmovement.org/benchmarking.

Abley Transportation Engineers (2008), Walkability Research Tools – Summary Report, Research Report 356, New Zealand Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/356.

Steve Abley and S. Turner (2011), Predicting Walkability, Research Report 452, New Zealand Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/452/docs/452.pdf.

Access Board (www.access-board.gov) is a U.S. federal agency that develops policies and recommendations for accessible design. Publications include Accessible Rights of Way: A Design Manual, 1999; ADA Accessibility Guidelines for Buildings and Facilities, 1998; Uniform Federal Accessibility Standards; and Designing Sidewalks and Trails for Access, Part One.

Access Exchange International (www.globalride-sf.org) is a non-profit organization that provides resources and coordination to develop cost-effective handicapped access in developing as well as developed countries.

ADIT (2013), Walking, Riding and Access to Public Transport: Supporting Active Travel in Australian Communities: Ministerial Statement; Australian Department of Infrastructure and Transport (www.infrastructure.gov.au); at www.infrastructure.gov.au/infrastructure/mcu/urbanpolicy/active_travel/index.aspx.

Inés Alveano-Aguerrebere, et al. (2018), “Bicycle Facilities that Address Safety, Crime, and Economic Development: Perceptions from Morelia, Mexico,” International Journal of Environmental Research and Public Health, Vol. 15(1) (doi:10.3390/ijerph15010001); at www.mdpi.com/1660-4601/15/1/1.

APMA (2007), “What Makes a Good Walking City,” Prevention Magazine, American Podiatric Medical Association and Prevention Magazine (www.prevention.com); at www.prevention.com/cda/article/what-makes-a-good-walking-city/db76f6cba5638110VgnVCM20000012281eac____/fitness/walking.

Carlos Balsas (2017), “Blending Individual Tenacity with Government’s Responsibility in the Implementation of US Non-motorized Transportation Planning (NMT),” Planning Practice & Research, Vol. 32, Issue 2, Pages 197-211 (http://dx.doi.org/10.1080/02697459.2017.1286920); at http://rsa.tandfonline.com/eprint/D2BHAM2bp2sAJDiU7cRM/full.

Keith Bartholomew and Reid Ewing (2009), “Land Use-Transportation Scenarios and Future Vehicle Travel and Land Consumption: A Meta-Analysis,” Journal of the American Planning Association, Vol. 75, No. 1, Winter (http://dx.doi.org/10.1080/01944360802508726).

David Bassett, et al. (2008), “Walking, Cycling, and Obesity Rates in Europe, North America, and Australia,” Journal of Physical Activity and Health, Vol. 5 (www.humankinetics.com/jpah/journalAbout.cfm), pp. 795-814; at http://policy.rutgers.edu/faculty/pucher/JPAH08.pdf.

David Bassett, et al. (2011), “Active Transportation and Obesity in Europe, North America, and Australia,” ITE Journal, Vol. 81/8, pp. 24-28; abstract at www.ite.org/itejournal/1108.asp.

Association of Pedestrian and Bicycle Professionals (www.apbp.org) provides information, support, training and credibility to non-motorized transportation professionals.

Gary Barnes and Kevin Krizek (2005a), Estimating Bicycling Demand, Transportation Research Board Annual Meeting (www.trb.org); at www.hhh.umn.edu/img/assets/11475/biking_demand.pdf.

Gary Barnes and Kevin Krizek (2005b), Tools for Predicting Usage and Benefits of Urban Bicycling, Humphrey Institute of Public Affairs, University of Minnesota (www.lrrb.org/pdf/200550.pdf).

Torsten Belter, Maike von Harten and Sandra Sorof (2013), Costs and Benefits of Cycling (Based on Desktop Research), Working Paper, SustraMM (Sustainable Transport for Managing Mobility; at http://enercitee.eu/files/dokumente/Subprojects/SUSTRAMM/SustraMM_Costs_and_benefits_of_cycling.pdf.

Lilah M. Besser and Andrew L. Dannenberg (2005), “Walking to Public Transit: Steps to Help Meet Physical Activity Recommendations,” American Journal of Preventive Medicine, Vo. 29, No. 4 (www.acpm.org); at www.cdc.gov/healthyplaces/articles/besser_dannenberg.pdf.

Chris Bradshaw (1993), Creating and Using a Rating System for Neighborhood Walkability, Ottawalk.

Werner Brog, Erhard Erl and Bruce James, “Does Anybody Walk Anymore?” (2003) Sustainable Transport: Planning for Walking and Cycling In Urban Environments (Rodney Tolley Ed.), Woodhead Publishing (www.woodhead-publishing.com), pp. 59-69

Ralph Buehler and John Pucher (2012), “Cycling to Work in 90 Large American Cities: New Evidence on the Role of Bike Paths and Lanes,” Transportation, Vol. 39, No. 2, pp. 409-432, DOI: 10.1007/s11116-011-9355-8.

Dan Burden (2003), Level of Quality (LOQ) Guidelines, Walkable Communities (www.walkable.org/library.htm); at www.tjpdc.org/transportation/walkability.asp. Shows graphically roadway design features that optimize pedestrian and cyclist access, safety and mobility, and transit station accessibility.

Max A. Bushell, et al. (2013), Costs for Pedestrian and Bicyclist Infrastructure Improvements: A Resource for Researchers, Engineers, Planners, and the General Public, Pedestrian and Bicycle Information Center (www.walkinginfo.org), Federal Highway Administration; at www.walkinginfo.org/download/PedBikeCosts.pdf.

Xinyu Cao, Susan L. Handy and Patricia L. Mokhtarian (2006), “The Influences Of The Built Environment And Residential Self-Selection On Pedestrian Behavior,” Transportation, Vol. 33, No. 1, pp. 1-20; at www.springerlink.com/content/7356r13j41266x67/.

CAP (2010), Benchmarking Active Transportation In Canadian Cities, Clean Air Partnership (www.cleanairpartnership.org); at http://torontocat.ca/sites/all/files/TCAT_ATBenchmarkingReport.pdf. This report evaluates non-motorized transport conditions and activities for various cities.

CAI-Asia (2011), Walkability Study in Asian Cities, Clean Air Asia (http://cleanairasia.org); at http://cleanairasia.org/walkability-study-in-asian-cities-4.

Sally Cairns, et al (2004), Smarter Choices - Changing the Way We Travel, UK Department for Transport (www.dft.gov.uk); at http://eprints.ucl.ac.uk/archive/00001224/01/1224.pdf. This comprehensive study provides detailed evaluation of the potential travel impacts and costs of various mobility management strategies. Includes numerous case studies.

Michael Carreno, Alexandra Willis and Steve Stradling (2002), Quality of Service Experienced by Pedestrians in Edinburgh, Transport Research Institute, Napier University (www.napier.ac.uk).

CATSIP (California Active Transportation Safety Information Pages) (http://catsip.berkeley.edu), a comprehensive online resource for improving walking and cycling conditions.

Nick Cavill and Adrian Davis (2007), Cycling & Health: What’s The Evidence?, Cycling England, Department for Transport (www.dft.gov.uk); at www.dft.gov.uk/cyclingengland/site/wp-content/uploads/2009/01/cycling_and_health_full_report.pdf.

Nick Cavill, et al. (2008), “Economic Analyses of Transport Infrastructure and Policies Including Health Effects Related to Cycling and Walking: A Systematic Review,” Transport Policy, Vol. 15, No. 5, pp. 291–304; at http://bit.ly/1mitpUX.

Nick Cavill, Andy Cope and Angela Kennedy (2009), Valuing Increased Cycling in the Cycling Demonstration Towns, Cycling England, Department for Transport (www.dft.gov.uk); at www.dft.gov.uk/cyclingengland/site/wp-content/uploads/2009/12/valuing-increased-cycling-in-the-cycling-demonstration-towns.pdf.

CDC (2004), Worksite Walkability: Are Your Employees Walking at Work?, Center for Disease Control (www.cdc.gov/nccdphp/dnpa/walkability). Includes a Worksite Walkability Audit Tool. Also see Andrew Dannenberg (2004), Assessing the Walkability of the Workplace: A New Audit, presented at the 4^th National Congress of Pedestrian Advocates, America Walks (www.americawalks.org).

Charlier Associates, Kevin J. Krizek and Ann Forsyth (2012), The Colorado Mile Markers: Recommendations for Measuring Active Transportation, Kaiser Permanente Colorado Health Initiative Team; at www.smartgrowthamerica.org/documents/cs/impl/co-performancemeasures-report.pdf.

Xuehao Chu (2003), The Fatality Risk of Walking in America: A Time-Based Comparative Approach, Walk21 Conference - Health, Equity and the Environment, Portland, Oregon (www.americawalks.org/PDF_PAPE/Chu.pdf).

David E. Clark (1997), Estimating Future Bicycle and Pedestrian Trips from a Travel Demand Forecasting Model, 1997 Compendium of Technical Papers, Institute of Transportation Engineers (www.ite.org), pp. 407-414.

S. Clark and A. Davies (2009), Identifying and Prioritising Walking Investment Through the PERS Audit Tool, TRL Walk21 (www.walk21.com/papers/TfL).

A. Clarke and L. Tracy (1995), Bicycle Safety-Related Research Synthesis, UNC Highway Safety Research Center, Federal Highway Administration, FHWA-94-062; at www.bicyclinginfo.org.

Kelly J. Clifton, et al. (2015), Development of a Pedestrian Demand Estimation Tool, National Institute for Transportation and Communities (http://ppms.otrec.us); at http://ppms.otrec.us/media/project_files/NITC-RR-677_Final_Report.pdf.

Gigi Cooper and Jennifer Danziger (2016), “Evaluating Return: A Benefit Calculator for Active Transportation Projects,” ITE Journal, Vol. 86, No. 9, pp. 25-29 (www.ite.org); at http://bit.ly/2fMtodt.

CORDIS (1999), Best Practice to Promote Cycling and Walking and How to Substitute Short Car Trips by Cycling and Walking, CORDIS Transport RTD Program, European Union (www.cordis.lu/transport/src/adonisrep.htm).

Joe Cortright (2009), Walking the Walk: How Walkability Raises Home Values in U.S. Cities, CEOs for Cities (www.ceosforcities.org); at www.ceosforcities.org/files/WalkingTheWalk_CEOsforCities1.pdf.

COWI (2009), Economic Evaluation Of Cycle Projects - Methodology And Unit Prices, Samfundsøkonomiske Analyser Af Cykeltiltag - Metode Og Cases and the accompanying note Enhedsværdier for Cykeltrafik, prepared by COWI for the City of Copenhagen (www.kk.dk/cyklernesby); at www.kk.dk/sitecore/content/Subsites/CityOfCopenhagen/SubsiteFrontpage/LivingInCopenhagen/CityAndTraffic/CityOfCyclists/CycleStatistics/socioeconomicbenefits.aspx.

CPF (2008), Economic Benefits of Cycling for Australia, Cycling Promotion Fund (www.cyclingpromotion.com.au); at www.cyclingpromotion.com.au/images/stories/downloads/CPF_CyclingBenefits.pdf.

CSE (2009), Footfalls: Obstacle Course to Livable Cities, Right To Clean Air Campaign, Centre For Science And Environment (www.cseindia.org); at www.indiaenvironmentportal.org.in/content/footfalls-obstacle-course-livable-cities.

Allison L. C. de Cerreño and My Linh H. Nguyen-Novotny (2006), Pedestrian and Bicyclist Standards and Innovations in Large Central Cities, Rudin Center for Transportation Policy & Management (www.wagner.nyu.edu/rudincenter); at http://wagner.nyu.edu/rudincenter/files/bikeped.pdf.

Jeroen Johan de Hartog, Hanna Boogaard, Hans Nijland and Gerard Hoek (2010), “Do the Health Benefits of Cycling Outweigh the Risks?” Environmental Health Perspectives, doi:10.1289/ehp.0901747, (http://ehp03.niehs.nih.gov/article/info%3Adoi%2F10.1289%2Fehp.0901747).

Jake Desyllas, et al. (2003), Pedestrian Demand Modelling of Large Cities: An Applied Example from London, Center for Advanced Spatial Modeling, University College London, Paper 62 (www.casa.ucl.ac.uk); at www.casa.ucl.ac.uk/working_papers/paper62.pdf.

DFT (various years), Traffic Advisory Leaflets: Cycle Facilities, Department for Transport (www.roads.dft.gov.uk/roadnetwork/ditm/tal/cycle/index.htm). Various information resources related to cycling promotion and planning.

DfT (2008), Cycling Demonstration Towns – Development of Benefit-Cost Ratios, by the UK Department for Transport (www.dft.gov.uk); at http://webarchive.nationalarchives.gov.uk/20110407094607/http:/www.dft.gov.uk/cyclingengland/cycling-cities-towns/results.

DfT (2014), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport (www.gov.uk/guidance/transport-analysis-guidance-webtag).

Jennifer Dill and John Gliebe (2008), Understanding and Measuring Bicycling Behavior: A Focus on Travel Time and Route Choice, Oregon Transportation Research and Education Consortium (OTREC); at www.lulu.com/items/volume_64/5687000/5687029/1/print/OTREC-RR-08-03_Dill_BicyclingBehavior_FinalReport.pdf.

Linda Dixon (1996), “Bicycle and Pedestrian Level-of-Service Performance Measures and Standards for Congestion Management Systems,” Transportation Research Record 1538, TRB (www.trb.org), pp. 1-9 (https://trid.trb.org/view/469373).

Frederick C. Dock, Ellen Greenberg and Mark Yamarone (2012), “Multimodal and Complete Streets Performance Measures in Pasadena, California,” ITE Journal (www.ite.org), Vol. 82/1, pp. 33-37; at www.ite.org/membersonly/itejournal/pdf/2012/JB12AA33.pdf.

Richard Dowling, et al. (2008), Multimodal Level Of Service Analysis For Urban Streets, NCHRP Report 616, Transportation Research Board (www.trb.org); at http://trb.org/news/blurb_detail.asp?id=9470; User Guide at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w128.pdf.

Dowling (2010), CompleteStreetsLOS: Multi-Modal Level-of-Service Toolkit, Dowling Associates (www.dowlinginc.com/completestreetslos.php). This software program automates the procedures described in NCHRP Report 616, Multimodal Level of Service for Urban Streets, for evaluating complete streets, context-sensitive design alternatives, and smart growth from the perspective of all users of the street.

Frank Douma and Fay Cleaveland (2008), The Impact of Bicycling Facilities on Commute Mode Share, Hubert H. Humphrey Institute of Public Affairs, University of Minnesota (www.hhh.umn.edu); at www.hhh.umn.edu/centers/slp/pdf/bicycling_facilities.pdf.

Ronald Eash (1999), “Destination and Mode Choice Models for Non-motorized Travel,” Transportation Research Record 1674, TRB (www.trb.org), pp. 1-8.

Economics of Active Transportation (www.ahtd.info/the_economics_of_active_transportation) by Active Healthy Transportation Developments (www.ahtd.info) provides information on walking and cycling economic benefits.

ECU (2004a), Physical Inactivity Cost Calculator (www.ecu.edu/picostcalc), College of Health & Human Performance, East Carolina University (www.ecu.edu); documentation at www.ecu.edu/picostcalc/pdf_file/Methods.pdf.

ECU (2004b), Physical Activity Facts and Figures, College of Health & Human Performance, East Carolina University (www.ecu.edu); at www.ecu.edu/picostcalc/pdf_file/FactsandFigures.pdf.

Debra Efroymson (2012), Moving Dangerously, Moving Pleasurably: Improving Walkability in Dhaka; Using a BRT Walkability Strategy to Make Dhaka’s Transportation Infrastructure Pedestrian-Friendly, Asian Development Bank (www.adb.org); at www.adb.org/sites/default/files/projdocs/2012/39335-012-reg-tacr-01.pdf.

Rune Elvik (2000), “What are the Relevant Costs and Benefits of Road Safety Measures Designed for Pedestrians and Cyclists?” Accident Analysis and Prevention, vol. 32, (www.elsevier.com/locate/aap), pp. 37-45.

Reid Ewing, et al. (2006), Measuring Urban Design Qualities: An Illustrated Field Manual, Robert Wood Johnson Foundation Active Living Research Program (www.activelivingresearch.org): at http://activelivingresearch.org/measuring-urban-design-qualities%E2%80%94-illustrated-field-manual.

FHWA (1993), National Bicycling and Walking Study (Case Study 15: The Environmental Benefits of Bicycling and Walking), Publication No. FHWA-PD-93-015 .U.S. Department of Transportation.

FHWA (2006), Shared-Use Path Level of Service Calculator, Turner-Fairbank Highway Research Center (www.tfhrc.gov), Federal Highway Administration, USDOT; at www.tfhrc.gov/safety/pedbike/pubs/05138.

FHWA (2012), “Traffic Monitoring For Non-Motorized Traffic,” Traffic Monitoring Guide, US Federal Highway Administration (http://fhwatmgupdate.camsys.com); at http://fhwatmgupdate.camsys.com/images/TMG_Ch4_aug20.pdf.

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Susan Handy, Gil Tal and Marlon G. Boarnet (2014), Policy Brief on the Impacts of Bicycling Strategies Based on a Review of the Empirical Literature, for Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).

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Michael Iacono, Kevin Krizek and Ahmed El-Geneidy (2008), Access to Destinations: How Close is Close Enough? Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes, Report 2008-11, University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=916.

ITDP (2011), Better Street, Better Cities: A Guide to Street Design in Urban India, Institute for Transportation and Development Policy (www.itdp.org); at www.itdp.org/betterstreets.

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Ann Jopson, Matthew Page and Batool Menaz (2007), Appraisal and Decision Making for Small Sustainable Urban Transport Measures?, 11th World Conference on Transport Research, Berkeley, California (www.wctrs.org).

Peter L. Jacobsen (2003), “Safety in Numbers: More Walkers and Bicyclists, Safer Walking and Bicycling.” Injury Prevention (http://ip.bmjjournals.com), Vol. 9, 2003, pp. 205-209; at http://injuryprevention.bmj.com/cgi/content/full/9/3/205.

Peter L. Jacobsen, F. Racioppi and H. Rutter (2009), “Who Owns the Roads? How Motorised Traffic Discourages Walking and Bicycling,” Injury Prevention, Vol. 15, Issue 6, pp. 369-373; http://injuryprevention.bmj.com/content/15/6/369.full.html.

Sonja Kahlmeier, Francesca Racioppi, Nick Cavill, Harry Rutter, and Pekka Oja (2010), “‘Health in All Policies’ in Practice: Guidance and Tools to Quantifying the Health Effects of Cycling and Walking,” Journal of Physical Activity and Health, Vol. 7, Supplement 1, pp. S120-S125; at www.euro.who.int/document/E93592.pdf.

Ian Ker (2001), “Deconstructing the Future: Assessing New Initiatives in Transport, Including Demand Management and Walking,” World Transport Policy and Practice, Vol. 7, No. 4 (www.ecoplan.org/wtpp).

Karl Kim, Napat Settachai, Eric Yamashita and Lauren Hallonquist (2008), Sit, Stand, or Sell: the Impact of Street Furniture on Pedestrian Level of Service, Transportation Research Board 87th Annual Meeting (www.trb.org). Also see, (2006), “Walking In Waikiki, Hawaii : Measuring Pedestrian Level Of Service In An Urban Resort District,” Transportation Research Record 1982, TRB (www.trb.org), pp. 104-112.

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Holly Krambeck and Jitendra (Jitu) Shah (2007), The Global Walkability Index: Talk the Walk and Walk the Talk, Clean Air Initiative for Asian Cities (www.cleanairnet.org); at www.cleanairnet.org/caiasia/1412/article-60499.html.

Kevin Krizek (2007), “Estimating the Economic Benefits of Bicycling and Bicycle Facilities: an Interpretive Review and Proposed Methods,” Essays on Transport Economics, Physica-Verlag HD, Springer (www.springerlink.com); at www.springerlink.com/content/l835v21829468170.

Kevin J. Krizek, et al. (2006), Guidelines for Analysis of Investments in Bicycle Facilities, Transportation Research Board, NCHRP Report 552 (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_552.pdf.

Kevin Krizek, et al. (2007a), Access to Destinations: Refining Methods for Calculating Non-Auto Travel Times, Report No. 2, Access to Destinations Study, University of Minnesota's Center for Transportation Studies (www.cts.umn.edu/access-study/publications).

Kevin Krizek, Michael Iacono, Ahmed M. El-Geneidy, Chen-fu Liao, Robert Johns (2009), Access to Destinations: Application of Accessibility Measures for Non-Auto Travel Modes, Report No. 9, Access to Destinations Study, University of Minnesota's Center for Transportation Studies (www.cts.umn.edu/access-study/publications); at www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=1808.

J. Richard Kuzmyak and Jennifer Dill (2012), “Walking and Bicycling in the United States: The Who, What, Where, and Why,” TR News 280, May-June; at http://onlinepubs.trb.org/onlinepubs/trnews/trnews280www.pdf.

J. Richard Kuzmyak, et al. (2014), Estimating Bicycling and Walking for Planning and Project Development: A Guidebook, NCHRP Report 770, Transportation Research Board (www.trb.org); at www.trb.org/main/blurbs/171138.aspx.

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James Leather, Herbert Fabian, Sudhir Gota and Alvin Mejia (2011), Walkability and Pedestrian Facilities in Asian Cities: State and Issues, Sustainable Development Working Paper, Asian Development Bank (www.adb.org); at http://cleanairinitiative.org/portal/sites/default/files/documents/ADB-WP17-Walkability-Pedestrian-Facilities-Asian-Cities.pdf.

Christopher Leinberger (2007), Footloose and Fancy Free: A Field Survey of Walkable Urban Places in the Top 30 U.S. Metropolitan Areas, Brookings Institution (www.brookings.edu); at www.brookings.edu/~/media/Files/rc/papers/2007/1128_walkableurbanism_leinberg/1128_walkableurbanism_leinberger.pdf.

John LePlante and Thomas Kaeser (2004), “The Continuing Evolution of Pedestrian Walking Speed Assumptions,” ITE Journal, Institute of Transportation Engineers (www.ite.org), Vol. 74, No. 9, September 2004, pp. 32-40; at http://library.ite.org/pub/e250c27c-2354-d714-5163-481e593e42db.

Yael M. Levitte (1999), Bicycle Demand Analysis – A Toronto Case Study, Transportation Research Board Annual Meeting (www.trb.org).

Todd Litman (2003), “Economic Value of Walkability,” Transportation Research Record 1828, Transportation Research Board (www.trb.org), pp. 3-11; at www.vtpi.org/walkability.pdf.

Todd Litman (2004), If Health Matters: Integrating Public Health Objectives into Transportation Decision-Making, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/health.pdf; previously published as, “Integrating Public Health Objectives in Transportation Decision-Making,” American Journal of Health Promotion, Vol. 18, No. 1 (www.healthpromotionjournal.com), Sept./Oct. 2003, pp. 103-108; at www.vtpi.org/AJHP-litman.pdf.

Todd Litman (2005), Whose Roads? Evaluating Bicyclists’ and Pedestrians’ Right to Use Public Roadways, VTPI (www.vtpi.org); at www.vtpi.org/whoserd.pdf.

Todd Litman (2006), “Managing Diverse Modes and Activities on Non-motorized Facilities: Guidance for Practitioners,” ITE Journal, Vol. 76, No. 6 (www.ite.org), June 2006, pp. 20-27; based on Todd Litman and Robin Blair (2005), Managing Personal Mobility Devices (PMDs) On Non-motorized Facilities, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/man_nmt_fac.pdf.

Todd Litman (2007), Guide to Calculating Mobility Management Benefits, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/tdmben.pdf.

Todd Litman (2009), Transportation Cost and Benefit Analysis: Techniques, Estimates and Implications, Victoria Transport Policy Institute (www.vtpi.org); particularly the “Barrier Effect” chapter at www.vtpi.org/tca/tca0513.pdf.

Todd Litman (2010), Short and Sweet: Analysis of Shorter Trips Using National Personal Travel Survey Data, VTPI (www.vtpi.org); at www.vtpi.org/short_sweet.pdf.

Todd Litman (2015), Comprehensive Evaluation of Completes Streets Policies: The Value of Designing Roads for Diverse Modes, Users and Activities, presented at the Threadbo 14 Conference (www.thredbo-conference-series.org), August 2015, Santiago, Chile; at www.vtpi.org/compstr.pdf.

Todd Litman (2011), Evaluating Active Transport Benefits and Costs: Guide to Valuing Walking and Cycling Improvements and Encouragement Programs, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/nmt-tdm.pdf; originally published as “Bicycling and Transportation Demand Management,” Transportation Research Record 1441, 1994, pp. 134-140.

Todd Litman (2017), Evaluating Transportation Diversity, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/choice.

Todd Litman and Steve Fitzroy (2005), Safe Travels: Evaluating Mobility Management Traffic Safety Impacts, VTPI (www.vtpi.org); at www.vtpi.org/safetrav.pdf.

Living Streets (2011), Making the Case for Investment in the Walking Environment, Living Streets Program (www.livingstreets.org.uk), University of the West of England and Cavill Associates; at www.livingstreets.org.uk/index.php/tools/required/files/download?fID=1668.

William R. Loudon, Mandi Roberts and Sarah Kavage (2007), Testing the Effectiveness of Bicycle and Pedestrian Access Improvements in Reducing Commute Vehicle Trips, Transportation Research Board Annual Meeting (www.trb.org).

Peter Loukopoulos and Tommy Gärling (2005), “Are Car Users too Lazy to Walk? The Relation of Distance Thresholds for Driving to the Perceived Effort of Walking,” Transportation Research Record 1926 (www.trb.org), pp. 206-211; http://pubsindex.trb.org/view.aspx?id=777894.

Roger Mackett (2000), How to Reduce the Number of Short Trips by Car, Centre for Transport Studies, University College London (www.ucl.ac.uk/transport-studies/shtrp.htm).

Theodore J. Mansfield and Jacqueline MacDonald Gibson (2015), “Health Impacts of Increased Physical Activity from Changes in Transportation Infrastructure: Quantitative Estimates for Three Communities,” BioMed Research International, Vol. 2015 (http://dx.doi.org/10.1155/2015/812325); at www.hindawi.com/journals/bmri/2015/812325.

Michelle J. Marcus (2008), Examining Correlations with Frequency of Walking Trips in Metropolitan Areas, Thesis, Georgia State University http://etd.gsu.edu/theses/available/etd-12052008-103930/unrestricted/MJMarcus_Thesis_12052008.pdf.

Andrew A. McDonald, et al. (2007), Estimating Demand for New Cycling Facilities in New Zealand, Land Transport New Zealand Research Report 340 (www.ltsa.govt.nz); at www.ltsa.govt.nz/research/reports/340.pdf.

Nathan McNeil (2010), Bikeability and the Twenty-Minute Neighborhood: How Infrastructure and Destinations Influence Bicycle Accessibility, Portland State University (www.ibpi.usp.pdx.edu); at www.ibpi.usp.pdx.edu/media/McNeil_Bikeability_June2010.pdf.

Measuring Walking (www.measuring-walking.org), describes internationally standardised monitoring methods of walking and public space.

Joseph Milazzo, et al (1999), Quality of Service for Interrupted Pedestrian Facilities in the 2000 Highway Capacity Manual, Transportation Research Board Annual Meeting (www.trb.org).

Erik Minge, et al. (2015), Bicycle and Pedestrian Data Collection Manual – Draft, University of Minnesota for the Minnesota Department of Transportation (www.dot.state.mn.us); at www.dot.state.mn.us/research/TS/2015/201533.pdf.

John A. Molino, et al. (2012), A Distance-Based Method to Estimate Annual Pedestrian and Bicyclist Exposure in an Urban Environment, Federal Highway Administration (www.fhwa.dot.gov); at www.fhwa.dot.gov/publications/research/safety/pedbike/11043/11043.pdf.

Peter D. Norton (2008), Fighting Trafﬁc: The Dawn of the Motor Age in the American City, MIT Press (www.mitpress.mit.edu); summary at http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=11471.

Anne Vernez Moudon (2001), Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel, Washington State Transportation Commission and U.S. Department of Transportation; at www.wsdot.wa.gov/research/reports/fullreports/519.1.pdf.

Anne Vernez Moudon and Chanam Lee (2003), “Walking and Bicycling: An Evaluation of Environmental Audit Instruments,” American Journal of Health Promotion, Vol. 18, No. 1 (www.healthpromotionjournal.com), Sept/Oct. 2003, pp. 21-37, at https://bit.ly/2Z01Owz.

NACTO (2011), Urban Bikeway Design Guide, National Association of City Transportation Officials (www.nacto.org); at http://nacto.org/cities-for-cycling/design-guide.

NACTO (2012), Urban Street Design Guide, National Association of City Transportation Officials (www.nacto.org); at http://nacto.org/urbanstreetdesignguide-overview. This guide provides comprehensive information on ways that cities can make streets safer, more livable, and more economically vibrant.

NACTO (2016), Global Street Design Guide, National Association of City Transportation Officials (www.nacto.org) and the Global Designing Cities Initiative (www.globaldesigningcities.org); at http://globaldesigningcities.org/publication/global-street-design-guide/streets/street-users.

NACTO (2017), Equitable Bike Share Means Building Better Places for People to Ride, National Association of City Transportation Officials (www.nacto.org); at https://nacto.org/wp-content/uploads/2016/07/NACTO_Equitable_Bikeshare_Means_Bike_Lanes.pdf.

Dan Nabors, et al. (2007), Pedestrian Road Safety Audit Guidelines and Prompt Lists, Pedestrian and Bicycle Information Center (www.pedbikeinfo.org), Federal Highway Administration Office of Safety; at http://drusilla.hsrc.unc.edu/cms/downloads/PedRSA%20-%20FINAL%20-%20high-quality.pdf.

Dan Nabors, et al. (2012), Bicycle Road Safety Audit Guidelines and Prompt Lists, Federal Highway Administration Office of Safety (http://safety.fhwa.dot.gov); at http://safety.fhwa.dot.gov/ped_bike/tools_solve/fhwasa12018/fhwasa12018.pdf.

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Krista Nordback, Michael Sellinger and Taylor Phillips (2017), Estimating Walking and Bicycling at the State Level, Final Report Nitc-Rr-708, National Institute for Transportation and Communities (http://ppms.trec.pdx.edu); at http://ppms.trec.pdx.edu/media/project_files/NITC_708_Washington_State_Pedestrian_and_Bicycle_Miles_Traveled.pdf.

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Brian Patterson and Sean Fadum (2013), Cycling Analysis in Metro Vancouver: Cycling Zone Analysis, Vancouver GIS Users Group (www.vancouvergis.org); at www.vancouvergis.org/docs/Urban_Systems_Cycle_Zone_Analysis.pdf.

PBIC, Image Library (www.pedbikeimages.org), by the Pedestrian and Bicycle Information Center (www.walkinginfo.org) provides an extensive collection of photographs related to walking and cycling.

PBQD (2000), Data Collection and Modeling Requirements for Assessing Transportation Impacts of Micro-Scale Design, TMIP, USDOT (www.bts.gov/tmip).

Pedestrian Quality Needs Study (www.walkeurope.org) developed resources for evaluating pedestrians’ quality needs and resources for incorporating them into urban planning.

Theodore Petritsch, et al. (2008a), Health Benefits of Bicycle Facilities, Paper 08-1230 Transportation Research Board Annual Meeting (www.trb.org); at http://pubsindex.trb.org/document/view/default.asp?lbid=847922.

Theodore Petritsch, et al. (2008b), Energy Savings from Provision of Bicycle Facilities, Paper 08-0219, Transportation Research Board 87th Annual Meeting (www.trb.org); at http://pubsindex.trb.org/document/view/default.asp?lbid=847915.

Rhonda Phillips, John Karachepone and Bruce Landis (2001), Multi-Modal Quality of Service Project, Florida Department of Transportation, Contract BC205 (www.dot.state.fl.us/Planning/systems/sm/los/default.htm).

Lee Pike (2011), Generation of Walking, Cycling and Public Transport Trips: Pilot Study, New Zealand Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/439/docs/439.pdf.

Richard Pratt, et al (2012), Pedestrian and Bicycle Facilities, Chapter 16, Traveler Response to Transportation System Changes, TCRP Report 95, TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_95c16.pdf.

John Pucher and Christian Lefevre (1996), The Urban Transportation Crisis in Europe and North America, MacMillan Press (London).

John Pucher and Ralph Buehler (2006), “Why Canadians Cycle More Than Americans: A Comparative Analysis Of Bicycling Trends And Policies,” Transport Policy, Vol. 13, May 2006, pp. 265–279; at www.vtpi.org/pucher_canbike.pdf.

John Pucher and Ralph Buehler (2008), “Making Cycling Irresistible: Lessons from the Netherlands, Denmark, and Germany,” Transport Reviews, Vol. 28, No. 4, July 2008; at www.vtpi.org/irresistible.pdf.

John Pucher, Jennifer Dill and Susan Handy (2010), “Infrastructure, Programs and Policies to Increase Bicycling: An International Review,” Preventive Medicine, Vol. 48, No. 2, February; prepared for the Active Living By Design Program (www.activelivingbydesign.org); at http://policy.rutgers.edu/faculty/pucher/Pucher_Dill_Handy10.pdf.

John Pucher, Ralph Buehler, David R. Bassett and Andrew L. Dannenberg (2010), “Walking and Cycling to Health: A Comparative Analysis of City, State, and International Data,” American Journal of Public Health, at http://ajph.aphapublications.org/cgi/reprint/AJPH.2009.189324v1.

John Pucher and Ralph Buehler (2011), Analysis of Bicycle Trends and Policies in Large North American Cities: Lessons For New York, University Transportation Research Center; at www.utrc2.org/research/assets/176/Analysis-Bike-Final1.pdf; summary at www.utrc2.org/research/assets/176/Bicycle-Brief1.pdf.

John Pucher, Ralph Buehler, and Mark Seinen (2011), “Bicycling Renaissance in North America? An Update and Re-Assessment of Cycling Trends and Policies,” Transportation Research A, Vol. 45, No. 8, pp. 451-475; at http://policy.rutgers.edu/faculty/pucher/TRA960_01April2011.pdf.

Piet Rietveld (2000), “Non-motorized Modes in Transport Systems: A Multimodal Chain Perspective for The Netherlands,” Transportation Research D, Vo. 5, No. 1, January 2000, pp. 31-36.

Stuart Reid (2003), Pedestrian Environments: A Systematic Review Process, Walk21 Conference - Health, Equity and the Environment, Portland, Oregon (www.americawalks.org/PDF_PAPE/Reid.pdf).

Stacy Rendall, Shannon Page, Fernke Reitsma, Elijah Van Houten and Susan Krumdieck (2011), “Quantifying Transport Energy Resilience: Active Mode Accessibility,” Transportation Research Record 2242, Transportation Research Board (www.trb.org), pp. 72-80; at http://amonline.trb.org/12l3tc/1.

Stacy Rendall, Peter Rose and Kurt Janssen (2012), Delivering Effective Cycle Facilities: Modelling Bicycle Route Choice in New Zealand, Abley Transportation Consultants (www.abley.com) Walk 21 Conference, Vancouver, BC.

Dorothy Robinson (2005), “Safety in Numbers in Australia: More Walkers and Bicyclists, Safer Walking and Bicycling,” Health Promotion Journal of Australia, Vol. 16, No. 1 (www.healthpromotion.org.au), April 2005, pp. 47-51.

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Collin Roughton, et al. (2012), Creating Walkable and Bikeable Communities: A User Guide to Developing Pedestrian and Bicycle Master Plans, Center for Transportation Studies at Portland State University (www.ibpi.usp.pdx.edu); at www.ibpi.usp.pdx.edu/media/IBPI%20Master%20Plan%20Handbook%20FINAL%20(7.27.12).pdf.

Sherry Ryan and Greg Lindsey (2013), Counting Bicyclists and Pedestrians to Inform Transportation Planning, Active Living Research (www.activelivingresearch.org); at www.activelivingresearch.org/bikepedcounts.

Kjartan Sælensminde (2004), “Cost-Benefit Analysis of Walking and Cycling Track Networks Taking Into Account Insecurity, Health Effects and External Costs of Motor Vehicle Traffic,” Transportation Research A, Vol. 38, No. 8 (www.elsevier.com/locate/tra), Oct. 2004, pp. 593-606; at www.toi.no/toi_Data/Attachments/887/sum_567_02.pdf.

Robert Salter, Subash Dhar and Peter Newman (2011), Technologies for Climate Change Mitigation: Transport Sector, Risø Centre on Energy, Climate and Sustainable Development, United Nations Environmental Program (www.uneprisoe.org); at http://tech-action.org/Guidebooks/TNAhandbook_Transport.pdf.

Marc Schlossberg, Asha Weinstein Agrawal, Katja Irvin and Vanessa Louise Bekkouche (2008), How Far, By Which Route, and Why? A Spatial Analysis Of Pedestrian Preference, Mineta Transportation Institute (www.transweb.sjsu.edu); at http://transweb.sjsu.edu/mtiportal/research/publications/documents/06-06/MTI-06-06.pdf

Robert Schneider, Robert S. Patten and Jennifer L. Toole (2005), A Case Study Analysis of Pedestrian and Bicycle Data Collection in United States Communities, Federal Highway Administration; at http://ite.org/Conference/papers/CB05B2104.pdf.

Gian-Claudia Sciara, Susan Handy and Marlon G. Boarnet (2014), Policy Brief on the Impacts of Pedestrian Strategies Based on a Review of the Empirical Literature, for Research on Impacts of Transportation and Land Use-Related Policies, California Air Resources Board (http://arb.ca.gov/cc/sb375/policies/policies.htm).

SDOT (2011), Neighborhood Business District Access Intercept Survey, Seattle Department of Transportation (www.seattle.gov); at www.seattle.gov/transportation/intercept_survey.htm.

Carly Seiff and Dana Weissman (2016), “Putting Active Transportation Performance Measures into Practice,” ITE Journal, Vol. 86, No. 3, pp. 28-33; at https://mydigitalpublication.com/publication/?i=292025.

Conor Semler, et al. (2016), Guidebook for Developing Pedestrian and Bicycle Performance Measures, Federal Highway Administration (www.fhwa.dot.gov/environment/bicycle_pedestrian); at http://bit.ly/2bMCkNL.

SFDPH (2008a), Pedestrian Environmental Quality Index, San Francisco Department of Public Health (www.sfphes.org); at www.sfphes.org/HIA_Tools_PEQI.htm

SFDPH (2008b), Bicycle Environmental Quality Index (BEQI), San Francisco Department of Public Health; at www.sfphes.org/HIA_Tools_BEQI.htm.

SFDPH (2008c), Pedestrian Injury Forecasting Model, San Francisco Department of Public Health (www.sfphes.org); at www.dph.sf.ca.us/phes/HIA_Tools_Ped_Injury_Model.htm.

Erica Simmons, et al. (2015), White Paper: Evaluating the Economic Benefits of Nonmotorized Transportation, FHWA-HEP-15-027, Pedestrian and Bicycle Information Center (www.pedbikeinfo.org); at http://bit.ly/1H7KuoK.

SKM and PWC (2011), Benefits of Inclusion of Active Transport in Infrastructure Projects, Queensland Department of Transport and Main Roads (www.tmr.qld.gov.au); at www.cbdbug.org.au/wp-content/uploads/north-brisbane-cycleway/135-00825-file8.pdf.

Ali Soltani and Andrew Allan (2005), A Computer Methodology For Evaluating Urban Areas For Walking, Cycling And Transit Suitability: Four Case Studies From Suburban Adelaide, Australia, Paper 272, Computers in Urban Planning and Urban Management (http://128.40.111.250/cupum/searchpapers/index.asp); at http://128.40.111.250/cupum/searchpapers/papers/paper272.pdf.

SQW (2007), Valuing the Benefits of Cycling: A Report to Cycling England, Cycling England, Department for Transport (www.dft.gov.uk); at www.dft.gov.uk/cyclingengland/site/wp-content/uploads/2008/08/valuing-the-benefits-of-cycling-full.pdf.

Tim Stonor, Maria Beatriz de Arruda Campos and Andrew Smith (2001), Towards a Walkability Index, Walk 21, Third International Conference, San Sabastian, Spain, Space Snytax (www.spacesnytax.com).

STPP (2003), Americans Attitudes Toward Walking and Creating More Walkable Communities, Surface Transportation Policy Project (www.transact.org).

Street Mobility Project (www.ucl.ac.uk/street-mobility). This program developed practical tools for measuring community severance (roads that create barriers to walking and cycling) and overcoming barriers to walking by older people.

Sam Swartz (2012), Steps to a Walkable Community: A Guide for Citizens, Planners, and Engineers, America Walks (www.americawalks.org/walksteps).

Fred Sztabinski (2009), Bike Lanes, On-Street Parking and Business A Study of Bloor Street in Toronto’s Annex Neighbourhood, The Clean Air Partnership (www.cleanairpartnership.org); at www.cleanairpartnership.org/pdf/bike-lanes-parking.pdf.

Rodney Tolley, ed. (2003), Sustainable Transport: Planning for Walking and Cycling in Urban Environments, Woodhead Publishing Ltd (www.woodhead-publishing.com).

Rodney Tolley (2011), Good For Busine$$ - The Benefits Of Making Streets More Walking And Cycling Friendly, Heart Foundation South Australia (www.heartfoundation.org.au); at www.heartfoundation.org.au/SiteCollectionDocuments/GoodforBusinessFINAL_Nov.pdf.

Ray Tomalty and Murtaza Haider (2009), Walkability and Health; BC Sprawl Report 2009, Smart Growth BC (www.smartgrowth.bc.ca); at www.smartgrowth.bc.ca/Portals/0/Downloads/sgbc-sprawlreport-2009.pdf.

TRB (1997 and 2010), Highway Capacity Manual, TRB (www.trb.org).

S.A. Turner, A. P. Roozenburg and T. Francis (2006), Predicting Accident Rates for Cyclists and Pedestrians, Land Transport New Zealand Research Report 289 (www.ltsa.govt.nz); at www.ltsa.govt.nz/research/reports/289.pdf.

S. Turner, R. Singh, P. Quinn and T. Allatt (2011), Benefits of New and Improved Pedestrian Facilities – Before and After Studies, Research Report 436, NZ Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/436/docs/436.pdf.

T.Y. Lin International (2012), Sharing the Road: Optimizing Pedestrian and Bicycle Safety and Vehicle Mobility, Michigan Department of Transportation (www.michigan.gov/mdot); at www.michigan.gov/mdot/0,4616,7-151-9622_11045_24249-279311--,00.html

T.Y. Lin International (2012), Best Design Practices for Walking and Bicycling in Michigan, Michigan Department of Transportation (www.michigan.gov/mdot) at www.michigan.gov/documents/mdot/MDOT_Research_Report_RC1572_Part6_387521_7.pdf.

UTTIPEC (2009), Pedestrian Design Guidelines: Don’t Drive…Walk!, Delhi Development Authority, New Delhi (www.uttipec.nic.in); at www.uttipec.nic.in/PedestrianGuidelines-30Nov09-UTTPEC-DDA.pdf.

David S. Vale, Miguel Saraiva and Mauro Pereira (2016), “Active Accessibility: A Review of Operational Measures of Walking and Cycling Accessibility,” Journal of Transport and Land Use, Vol. 9, No. 1; at https://www.jtlu.org/index.php/jtlu/article/view/593.

Darren Walton and Stephen J. Murray (2012), Minimum Design Parameters for Cycle Connectivity, Report 432, NZ Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/432/docs/432.pdf.

Walk Friendly Communities (www.walkfriendly.org) is a national program sponsored by the U.S. Department of Transportation to encourage towns and cities to establish a high priority for supporting safer walking environments. The Resources section provides useful information for bicycle and pedestrian planning and analysis.

Walkability Tools Research Webpage (www.levelofservice.com) is a Land Transport New Zealand website concerned with methods for measuring walkability.

WalkIt: The Walking Resources Database (www.walkit.info) provides extensive resources for pedestrian planning in urban development, local transport, health and recreation.

WalkScore (www.walkscore.com) calculates the walkability of a location based on proximity to public services such as stores, schools and parks. For more information see How Walkscore Works (www.redfin.com/how-walk-score-works).

Jay Walljasper (2013), Walking As A Way Of Life Movement For Health & Happiness, Everybody Walks ( (www.everybodywalk.org); at www.everybodywalk.org/media_assets/WalkingAsAWayOfLife1_Final.pdf.

M. Wedderburn (2013), Improving The Cost-Benefit Analysis of Integrated PT, Walking And Cycling, Research Report 537, NZ Transport Agency (www.nzta.govt.nz); at www.nzta.govt.nz/resources/research/reports/537/docs/537.pdf.

Asha Weinstein and Paul Schimek (2005), How Much Do Americans Walk? An Analysis Of the 2001 NHTS, Transportation Research Board Annual Meeting (www.trb.org).

Asha Weinstein Agrawal, Marc Schlossberg and Katja Irvin (2008), “How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference,” Journal of Urban Design, Volume 13, Issue 1, February, pp. 81-98; at http://transweb.sjsu.edu/mtiportal/research/publications/summary/0606.html.

Barry Wellar (1998), Walking Security Index; Final Report, Geography Department, University of Ottawa (http://aix1.uottawa.ca/~wellarb/publications.htm).

WHO (2013), Pedestrian Safety: A Road Safety Manual For Decision-Makers And Practitioners, World Health Organization (www.who.int); at http://who.int/roadsafety/projects/manuals/pedestrian/en/index.html.

WHO (2014), Health Economic Assessment Tool for Cycling and Walking, World Health Organization Region Office Europe (www.euro.who.int); at http://tinyurl.com/3k8syj2.

Meghan Winters, Melissa C. Friesen, Mieke Koehoorn and Kay Teschke (2007), “Utilitarian Bicycling: A Multilevel Analysis of Climate and Personal Influences,” American Journal of Preventive Medicine, Vol. 32, No. 1, January.

Meghan Winters and Adam Cooper (2008), What Makes a Neighbourhood Bikeable, Cycling In Cities, University of British Columbia (www.cher.ubc.ca/cyclingincities); at www.cher.ubc.ca/cyclingincities/pdf/WhatMakesNeighbourhoodsBikeable.pdf.

Charles Zegeer, et al. (2010), Pedestrian Safety Strategic Plan: Recommendations for Research and Product Development, Federal Highway Administration Office of Safety (http://safety.fhwa.dot.gov); at https://bit.ly/2wuJqPa.

This Encyclopedia is produced by the Victoria Transport Policy Institute to help improve understanding of Transportation Demand Management. It is an ongoing project. Please send us your comments and suggestions for improvement.

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