Performance Evaluation

Practical Indicators For Evaluating Progress Toward Planning Objectives

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TDM Encyclopedia

Victoria Transport Policy Institute

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Updated 21 March 2019

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This chapter describes Performance Evaluation, which applies specific performance indicators to measure progress toward specific goals and objectives.

Wit and Humor

“Sustainability is the next great game in transportation. The game becomes serious when you keep score” – www.greenroads.us

Description

Performance Evaluation refers to a monitoring and analysis process to determine how well policies, programs and projects perform with regard to their intended goals and objectives. Performance indicators (also called measures of effectiveness) are specific measurable outcomes used to evaluate progress toward established goals and objectives. A performance index is a set of performance indicators in a framework designed to facilitate analysis. Commonly used performance indices include school grades, sports ratings, economic productivity indicators, and investment rating systems.

An organization’s performance can be evaluated at various levels:

• Process – the types of policies and planning activities, such as whether the organization has a process for collecting and publishing performance data, and public involvement.
• Inputs – the resources that are invested in particular activities, such as the level of funding spent on various activities or modes.
• Outputs – direct results, such as the miles of sidewalks, paths and roads, and the amount of public transit service provided.
• Outcomes – ultimate results, such as the number of miles traveled and mode split, average travel speeds, congestion and crowding, number of accidents and casualties, energy consumption, pollution emissions, and user satisfaction.

It is often best to use some of each type of performance indicators. For example, when evaluating the performance of a government agency or jurisdiction it may be appropriate to develop a performance index that include indicators of process, inputs, outputs and outcomes.

Performance indices have many practical applications including trend analysis, comparisons, target setting, and incentives (such as rewards) for managers and employees. They provide a navigation system that indicates where the organization is, where it wants to go, and how to get there. They help identify developing problems and the effectiveness of solutions. Indices can present data in various ways:

• Per capita
• Per vehicle
• Trends over time
• Comparisons with peers (other jurisdictions or groups)

Performance indicators must be carefully selected to accurately reflect goals and identify problems. Inappropriate or incomplete indicators can misdiagnose problems and misdirect decision-making (DeRobertis, et al. 2014). For example, an index that only considers quantity will encourage organizations to produce abundant but inferior output, while an index that only considers quality can result in high quality but inadequate production quantity.

Conventional Performance Indicators

Conventional indicators tend to evaluate transport system performance based on motor vehicle travel conditions (Markow 2012):

· Roadway Level-of-Service (LOS), which is an indicator of vehicle traffic speeds and congestion delay at a particular stretch of roadway or intersection.

· Average traffic speeds.

· Average congestion delay, measured annually per capita.

· Parking convenience and affordability (low price).

· Crash rates per vehicle-mile.

Because they focus on motor vehicle travel these methods favor automobile-oriented improvements over other objectives and solutions (DeRobertis, et al. 2014). For example, they justify road and parking facility capacity expansion that tends to create Automobile Dependent transport and land use systems, increasing per capita vehicle travel and reducing the viability of walking, cycling and public transit. This increases per capita vehicle ownership and use, increasing resource consumption, pollution emissions and land consumption, and exacerbating the transport problems facing non-drivers.

By evaluating impacts per vehicle-mile rather than per capita, they do not consider increased vehicle mileage to be a risk factor and they ignore vehicle traffic reductions as possible solution to transport problems. For example, from this perspective an increase in per capita vehicle crashes is not a problem provided that there is a comparable increase in vehicle mileage. Increased vehicle travel can even be considered a traffic safety strategy if it occurs under relatively safe conditions, because more safe miles reduce per-mile crash and casualty rates.

Comprehensive Performance Indicators

More comprehensive performance indices are important for multi-modal, Transportation Demand Management, Complete Streets design, and Sustainable Transportation planning. These can be selected and modified as needed to reflect the values, needs and conditions of a particular planning situation. Below are examples.

· Accessibility (ability to reach desired goods, services and activities), including the travel time and costs required by various users to reach activities and destinations such as work, education, public services and recreation (CTS 2010)

· Land Use Density and Mix - Number of job opportunities and commercial services within 30-minute travel distance of residents.

· Children’s accessibility - Portion of children who can walk or bicycle to Schools, shops and parks from their homes.

· Commute speed - Average commute travel time and Congestion delay.

· Transport diversity - Variety and quality of transport Options available in a community.

· Mode share - Portion of travel made by walking, cycling, rideshare, public transit and telework.

· Streetscape Quality – The quality of travel by various modes, plus impacts on local businesses and residents (Livability)

· Transit service quality – Public transit service quality, including coverage (portion of households and jobs within 5-minute walking distance of 15-minute transit service), service frequency, comfort (portion of trips in which passenger can sit and portion of transit stops with shelters), affordability (fares as a portion of minimum wage income), information availability, and safety (injuries per billion passenger-miles)

· Consumer Transport Costs and Affordability - Portion of household expenditures devoted to transport, including vehicle expenses, fares, residential parking costs, and taxes devoted to transport; particularly by people who are economically, socially and physically disadvantaged.

· Facility costs - Per capita expenditures on roads, traffic services and parking facilities (Transport Costs).

· Freight and commercial transport efficiency – Speed, quality and affordability of freight and commercial transport. 

· Market Efficiency - Degree to which transport systems reflect market principles such as prices that reflect full costs and neutral tax policies.

· Planning Practices - Degree to which transport institutions reflect Least-cost planning and investment practices. Higher is better.

· User Evaluation – Overall user satisfaction with their transportation system.

· Planning process - Range of impacts and options considered in the planning process, and quality of public involvement. 

· Health and fitness - Portion of population that regularly uses active transport modes (walking and cycling).

· Community Livability - Degree to which transport activities increase community livability (local environmental quality).

· Basic Mobility and Access – Quality of transport to access socially valuable activities such as medical services, education, employment and essential shopping, particularly for disadvantaged populations.

· Equity - Degree to which transport policies reflect equity objectives.

· Multi-Modal Level-of-Service Indicators evaluate the quality of various transport modes from a users perspective. This helps create a more neutral planning decisions compared with current practices which apply roadway LOS ratings but no comparable indictors for other modes.

· Energy Consumption and Pollution Emissions – the amount of transportation energy used and pollutants emitted.

· Habitat protection - Preservation of high-quality wildlife habitat (wetlands, old-growth forests, etc.) from loss due to transport facilities and development (Land Use Evaluation). Higher is better.

Multi-Modal Performance Indicators

There are three general types of performance indicators:

· Service quality – These reflect the quality of service experienced by users.

· Outcomes – These reflect outcomes or outputs, such as changes in travel activity or costs.

· Cost efficiency – These reflect the ratio of inputs (costs) to outputs (desired benefits).

Each type is important. Service quality reflects users’ perspectives. Outcomes reflect planning objectives. Cost efficiency reflects economic performance. Table 1 illustrates examples of these indicators for various transport modes. Level-Of-Service (LOS) ratings are now available for evaluating most modes.

Table 1            Examples of Performance Indicators for Various Modes

Mode	Service Quality	Outcomes	Cost Efficiency
Walking	Sidewalk/path supply Pedestrian LOS Crosswalk conditions	Pedestrian mode split Avg. annual walk distance Pedestrian crash rates	Cost per sidewalk-km Cost per walk-km Cost per capita
Cycling	Bike path and lane supply Cycling LOS Path conditions	Bicycle mode split Avg. annual cycle distance Cyclist crash rates	Cost per path-km Cost per cycle-km Cost per capita
Automobile	Roadway supply Roadway pavement condition Roadway LOS Parking availability	Avg. auto trip travel time Vehicle energy consumption and pollution emissions Motor vehicle crash rates	Cost per lane-km Cost per vehicle-km User cost per capita External cost per capita
Public transit	Transit supply Transit LOS Transit stop and station quality Fare affordability	Transit mode split Per capita transit travel Avg. transit trip travel time Transit crash and assault rates	User cost per pass.-km User cost per capita Subsidy per capita
Taxi	Taxi supply Average response time	Taxi use Taxi crash and assault rates	Cost per taxi-trip External costs
Multi-modal	Transport system integration Accessibility from homes to common destinations User survey results	Total transportation costs Total average commute time Total crash casualty rates	Total cost passenger-km Total cost per capita External cost per capita
Aviation	Airport supply Air travel service frequency Air travel reliability	Air travel use Air travel crash rates	Cost per trip External costs Airport subsidies
Rail	Rail line supply Rail service speed and reliability	Rail mode split Rail traffic volumes Rail crash rates	Cost per rail-km Cost per tonne-km External costs
Marine	Marine service supply Marine service speed and reliability	Marine mode split Marine traffic volumes Marine accident rates	Cost per tonne-km Subsidies External costs

This table illustrates various types of performance indicators.

TDM Performance Indicators

Below are performance indicators suitable for evaluating TDM programs (Schreffler 2000). These indicators can be defined for a particular time (such as peak-hour) and geographic location (such as a particular destination, district or region).

· Awareness – the portion of potential users who are aware of a program or service.

· Participation – the number of people who respond to an outreach effort or request to participate in a program.

· Utilization – the number of people who use a service or alternative mode.

· Mode share – the portion of travelers who use each transportation mode.

· Mode shift – the number or portion of automobile trips shifted to other modes.

· Average Vehicle Occupancy (AVO): Number of people traveling in private vehicles divided by the number of private vehicle trips. This excludes transit vehicle users and walkers.

· Average Vehicle Ridership (AVR): All person trips divided by the number of private vehicle trips. This includes transit vehicle users and walkers.

· Vehicle Trips or Peak Period Vehicle Trips: The total number of private vehicles arriving at a destination (often called “trip generation” by engineers).

· Vehicle Trip Reduction – the number or percentage of automobiles removed from traffic.

· Vehicle Miles of Travel (VTM) Reduced – the number of trips reduced times average trip length.

· Energy and emission reductions – these are calculated by multiplying VMT reductions times average vehicle energy consumption and emission rates.

· Cost Per Unit of Reduction – these measures of cost-effectiveness are calculated by dividing program costs by a unit of change. For example, the cost effectiveness of various TDM programs could be compared based on cents per trip reduced, or ton of air pollution emission reductions. However, as described later, cost-effectiveness analysis that only considers direct impacts and a single objective may overlook additional costs and benefits to participants and society. For example, two TDM programs may have the same direct costs per unit of emission reduction, but differ significantly in terms of consumer costs, consumer travel options, traffic congestion, parking costs, crash risk and land use impacts.

Percentage Versus Points

There is often confusion between a percentage change, and percentage points (or just points) change. Percentage refers to a hundredth of a category, such as motorists who drive alone. Percentage points refers to a hundredth of all categories together.

For example, a worksite previously had 85 commuters who drove alone, 10 that rode transit and 5 that walks or biked. A new incentive such as Parking Cash Out resulted in 65 car drivers, 25 transit riders, and 10 walk/bike commuters. This could be described as a 24% reduction in automobile trips, a 150% increase in transit trip, and a 100% increase in walk/bike trips, or it can also be described as a 15-point shift from automobile to transit and a 5-point shift from automobile to walk/bike. Either is appropriate, but it is important to be clear and consistent about which is used in a particular analysis.

Evaluation studies can compare performance indicator values before-and-after, over time (for example, over months or years), with-and-without (for example, comparing performance indicators at a worksite or area that has a TDM program with otherwise comparable sites that do not have such programs, or with regional averages).

A variety of methods can be used to collect the data needed for performance evaluation, including general travel surveys and Statistics, participant Surveys, parking lot counts, traffic counts, and focus groups. Before-and-after and with-and-with comparisons require the collection of good baseline data, or the use of readily-available statistics. It is important to consider such data collection needs when creating an evaluation plan.

How It Is Implemented

Performance Evaluation is generally implemented by a Planning organization or TDM Program as part of Evaluation activities. Planners should identify appropriate indicators that measure progress toward stated goals and objectives, taking into account the quality of available data and the costs of collecting any additional data. Litman (2005) describes factors to consider when selecting indicators.

Best Practices

Transportation professionals have developed guidance for selecting indicators for transportation program evaluation (TRB 2008), strategic planning (CTE 2008), and sustainable transport planning (Gudmundsson 2001; Litman 2005; STI 2008). The following principles should be applied when selecting transportation performance indicators (Hart, 1997; Marsden, Kelly and Snell, 2006):

• Comprehensive – Indicators should reflect various economic, social and environmental impacts, and various transport activities (such as both personal and freight transport).

• Data quality – Data collection practices should reflect high standards to insure that information is accurate and consistent.

• Comparable – Data collection should be standardized so the results are suitable for comparison between various jurisdictions, times and groups. Indicators should be clearly defined. For example, “Number of people with good access to food shopping” should specify ‘good access’ and ‘food shopping.’

• Easy to understand – Indicators must useful to decision-makers and understandable to the general public. The more information condensed into a single index the less meaning it has for specific policy targets (for example, Ecological Footprint analysis incorporates many factors) and the greater the likelihood of double counting.

• Accessible and transparent – Indicators (and the raw data they are based on) and analysis details should be available to all stakeholders.

• Cost effective – The suite of indicators should be cost effective to collect. The decision-making worth of the indicators must outweigh the cost of collecting them.

• Net effects – Indicators should differentiate between net (total) impacts and shifts of impacts to different locations and times.

· Performance targets – select indicators that are suitable for establishing usable performance targets.

Transportation performance evaluation should generally be based on Accessibility (the ability to reach desired services and activities) rather than just mobility (physical movement), because access is the ultimate goal of most transport activity (Table 2). Conventional transportation performance indicators, such as roadway Level-of-Service (LOS) ratings and average traffic speeds, primarily considered motor vehicle traffic conditions. They have been criticized for ignoring or undervaluing other impacts and objectives, such as cost efficiency, equity, community livability and environmental quality (SFCTA, 2008). In recent years, many transport organizations have developed more comprehensive performance indicator sets that better reflect diverse planning goals and objectives (WSDOT, 2008; Litman, 2007).

Table 2            Performance Indicators (Measuring Transportation)

Traffic Oriented	Mobility Oriented	Access Oriented
Road system quality (e.g., roadway Level-Of-Service). Average traffic speed and congestion delay. Parking convenience. Vehicle use affordability. Vehicle-km crash and pollution rates.	Transit service quality. Transit fare affordability. Rideshare Programs. Walk and bike facility quality. Transport system integration (e.g. ability to carry packages and bicycles on transit vehicles). Passenger-km crash and pollution rates. Universal Design.	Door-to-door commute times. Portion of homes and worksites with shops, public services and transit within convenient walking distance. Quality and availability of telephone and Internet service. Quality of delivery services. Per capita total transportation costs and overall transport affordability. Per capita crash and pollution rates.

This table compares different types of performance indicators. Transportation Demand Management tends to require mobility-oriented and access-oriented indicators.

Streets performance evaluation can include factors related to both travel and impacts on nearby businesses and residents. York City Department of Transportation uses the following goals, strategies and performance indicators when evaluating city streets:

Table 3            New York City Street Performance Metrics (NYCDTO 2012)

Goals	Strategies	Metrics
Safety Accommodate all users Create great public spaces	Design safe streets Build great public spaces Improve bus service Reduce delay and speeding Efficiently manage parking and loading	Motorist, pedestrian, and cyclist crashes and injuries Vehicle, bus passenger, bicycle rider, and public space user volumes Traffic volumes Travel speeds Traffic speeds (not too slow, but not too fast) Economic vitality (retail sales, building vacancies, visitors) Bus ridership and travel speeds User satisfaction Environmental and public health quality Double parking and parking duration

New York City has established these goals, strategies and metrics for evaluating the performance of city streets.

Some public transit agencies are developing performance indicators that reflect service comfort and reliability, such as frequency and degree of crowding, and percentage of on-schedule arrival (Schmitt 2016).

Relationships With Other TDM Strategies

For more information on the concepts and techniques discussed in this chapter see Measuring Transportation, TDM Evaluation, TDM Planning, Comprehensive Transportation Evaluation, Multi-Modal Level-of-Service Indicators, Modeling Improvements, Equity Analysis, Transportation Statistics, Data Collection, and Evaluating Transportation Diversity.

Wit and Humor

A wealthy old lady brings her poodle on a safari in Africa. One day the dog wanders out of camp and then sees a hungry looking leopard heading rapidly in her direction. The poodle thinks, “Oh, oh! I better think of some way to defend myself or I’ll soon be cat dinner!” Noticing some bones on the ground close by, she settles down to chew noisily on them as the big cat approaches. Just as the leopard is about to leap, the dog exclaims loudly, “Boy, that was one delicious leopard! I wonder if there are any more around here?”

Hearing this, the young leopard halts his attack and he slinks away. “Whew!” says the leopard, “That was close! That old poodle nearly had me!” A monkey who saw all of this occur from a nearby tree figures he can put this knowledge to good use and trade it for protection from the leopard. The monkey soon catches up with the leopard, explains the poodle’s trick and strikes a deal for himself with the leopard. The young leopard, furious at being fooled, says, “Monkey, hop on my back and let me show you what happens to conniving canines!”

The poodle sees the leopard coming with the monkey and realizes what is happening. Instead of running the dog sits down with her back to her attackers, pretending she hasn’t seen them. When they get close enough to hear, the poodle says loudly to herself, “Where’s that darn monkey? I sent him off an hour ago to bring me another leopard!”

Examples and Case Studies

TRB (2008), CTE (2008) and Litman (2005) provide numerous examples of transportation performance indicators.

Traffic Stress Level Analysis

The report, Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility (Mekuria, Appleyard and Nixon 2017) developed a Level of Traffic Stress (LTS) performance indicator which measures how the streets functioned for active and public transport modes. This information can be used to evaluate the relative attractiveness of different modes, and therefore the value of roadway design improvements and trade-offs between different modes.

Performance Indicators (Grant, et al. 2011)

Table 4 summarizes examples of local and regional transport system performance indicators (called measures in the report) identified for highway congestion management.

Table 4            Local and Regional Transport System Performance Indicators           

Type of Indicator	Localized or Corridor Scale	Regional or System Scale
Congestion intensity: volume/capacity measures	• Volume-to-capacity ratio (V/C), for segment • Level of Service (LOS), for a segment or intersection	• Number or share of roadway miles operating at V/C ratio over 1.0 • Number/share of roadway miles at LOS E or worse • Number of intersections at LOS E or worse
Congestion intensity: travel time measures	• Travel speed (miles per hour) • Average delay time (the difference between travel time and acceptable or free-flow travel time) • Travel time index (ratio of peak-period to non-peak-period travel time)	• Average regional commute time (by mode) • Total excess delay time (wasted travel time) • Share of roads experiencing travel time index over 2.0
Congestion duration	• Hours of travel per day at V/C ratio over 1.0 • Hours of travel per day at LOS E or worse	• Number or share of roadway miles experiencing more than 3 hours of congestion per day on average
Congestion extent: vehicle measures	• Number of vehicles experiencing LOS E or worse, for a segment	• Number or share of vehicle miles traveled at LOS E or worse, regionally
Congestion extent: delay measures	• Total delay on roadway (average delay time per vehicle x number of vehicles)	• Total excess delay time (wasted travel time)
Reliability	• Planning time index – ratio of 95th percentile travel time to free flow travel time • Buffer index – ratio of difference between 95th percentile travel time and average travel time, divided by average travel time • Crash rate by route or intersection • Number of incidents	• Share of freeway segments with planning time index over a certain threshold • Average buffer index for commute trips • Crash rate regionally
Transit travel conditions	• Transit crowding • Transit on-time performance (by route)	• Percentage of buses/trains exceeding a certain crowding level. • Percentage of buses arriving on-time regionally
Availability or service level of modes	• Existence of sidewalks • Existence of bicycle lanes or paths • Existence of pedestrian features (countdown pedestrian signals, painted crosswalks, etc.) • Existence of high-frequency bus services	• Miles of sidewalks or share of roads with sidewalks regionally • Miles of bicycle lanes or paths or share of roads designated as bicycle routes regionally • Number of intersections with pedestrian features
Geographic accessibility	• Number of jobs/households within a defined distance or travel time from location	• Share of regional jobs within ¼ mile of transit • Share of regional households within ¼ mile of transit
Land use	• Jobs-housing balance (ratio) within area/zone	• Jobs-housing balance (ratio) across each area
Congestion cost	• Wasted fuel (gallons) • Wasted money (value of travel time, fuel, vehicle operating costs)	• Wasted fuel (gallons) • Wasted money (value of travel time, fuel, vehicle operating costs)
Traveler information	• Existence of variable message signs (or other traveler information) by route • Existence of ―next bus‖ information by bus route	• Share of freeways regionally with variable message signs • Share of bus stops regionally with ―next bus‖ information
Incident duration	• N/A (data not typically available for specific locations, with limited exceptions)	• Mean time for responders to arrive on scene after notification • Mean incident clearance time

Transit Service Quality (De Oña, et al. 2016)

Different types of transit users demand different types of services. De Oña, et al. (2016) used cluster analysis to identify various transit user groups and identify the attributes of each group in order to allow transit agencies target services and marketing to each group. Specifically, six different user groups were identified based on their income, ability to drive (having a driver’s license and vehicle available), and preferences (whether they prefer driving or transit). It can be concluded that users who choose to travel by Metro but who have the option to travel by private car are more satisfied with the service, whereas users who use only the Metro because they do not have other alternatives (captive users) are more critical towards the service. This is a comforting result, which suggests that if transit operators offer services characterized by high levels of quality, public transport can become a real alternative to private cars.

Copenhagenize Index (http://copenhagenize.eu/index)

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. Modal Share For Bicycles: What percentage of modal share is made up by cyclists? Rated from under 1% to over 25%.
8. 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%+.
9. 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.
10. 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.
11. Social Acceptance: How do drivers and the community at large regard urban cyclists? Rated from no social acceptance to widespread social acceptance.
12. 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.
13. 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.

AllTransit (http://alltransit.cnt.org)

AllTransit is a multi-facetted transit performance index system that provides quantitative data on transit connectivity, access, and frequency for 805 U.S. transit agencies. This information can be used transit service and transit-oriented development evaluation and planning.

Multi-Modal Urban Transportation Performance Indicators (Wilbur Smith 2008)

The study, Traffic & Transportation Policies and Strategies in Urban Areas in India (Wilbur Smith 2008) developed a Transport Performance Index for evaluating urban transportation systems and prioritizing system improvements in Indian cities. It consists of the following factors:

· Public Transport Accessibility Index (the inverse of the average distance (in km) to the nearest bus stop/railway station (suburban/metro).

· Service Accessibility Index (% of Work trips accessible in 15 minutes time).

· Congestion Index (average peak-period journey speed relative to a target journey speed).

· Walkability Index (quantity and quality of walkways relative to roadway lengths).

· City Bus Transport Supply Index (bus service supply per capita).

· Para-Transit Supply Index (para-transit vehicle  supply per capita).

· Safety Index (1/traffic fatalities per 100,000 residents).

· Slow Moving Vehicle (Cycling) Index (availability of cycling facilities and cycling mode share).

· On-street Parking Interference Index (1/(portion of major road length used for on-street parking + on-street parking demand).

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.

Walking and Cycling Performance Indicators (Roughton, et al. 2012)

The report, Creating Walkable and Bikeable Communities: A User Guide to Developing Pedestrian and Bicycle Master Plans, identifies the following non-motorized transport performance indicators.

Infrastructure

• Total miles of bikeways

• Miles of bikeways catering to each type of bicyclist (i.e. Strong and Fearless, Enthusiastic and Confident, and Interested but Concerned)

• Percent of households within one quarter mile of a bicycle facility

• Percent of buses equipped with bicycle racks

• Percent of transit stops with bicycle parking or secure bicycle parking

• Percent of new developments that include secure bicycle parking or other end-of-trip facilities

• Number of bicycle parking spaces

• Percent of roadways with sidewalks

• Number of miles of sidewalk infill per year

• Percent of intersections up to current ADA standards

• Number of transit stops with pedestrian amenities

• Percent of new developments meeting pedestrian standards

• Number of bridges with dedicated bicycle and pedestrian facilities

• Number of miles of trails/multi-use paths

Programs

• Percent of schools served by Safe Routes to Schools program

• Number of safety trainings offered per year

• Number of enforcement efforts per year

• Attendance at Ciclovia or Open Streets events

• Number of households participating in individualized marketing programs

• Mode shift resulting from individualized marketing programs

Use And Safety

• Mode share for work trips

• Mode share for all trips

• Number of walking and bicycling trips per day along key corridors

• Bicycle and pedestrian crash rates

• Percent of bicyclists that are women, youth or seniors

• Average trip distance across all modes

• Number of trips made by bike share

Public Opinion

• Percent of residents satisfied with the safety and comfort of existing bicycle and/or pedestrian facilities

• Percent of residents interested in walking and bicycling more frequently

Cross-Country Review of Transport Agency Indicators (Karlaftis and Kepaptsoglou 2012)

A study for the International Transport Forum analyzed the performance indicators used by transport agencies in various countries. They find that most agencies use indicators that reflect infrastructure quality and preservation, mobility and accessibility, support for economic development, safety and security, environmental sustainability. Few indicators are multi-modal (for example, there is little consideration of non-motorized transport), and few indicators reflect social equity objectives such as improved accessibility for non-drives or accommodation of people with disabilities. They conclude that standardizing performance indicators and targets among different agencies worldwide would be difficult but useful for benchmarking and resource allocation.

State Performance Evaluation (PCT 2011)

The report, Measuring Transportation Investments: The Road To Results, evaluates how well U.S. states define and consider various performance goals in their investment planning. The research examines six policy areas considered important for evaluating economic value, listed below.

1. Safety. The ability of the transportation system to allow people and goods to  move freely without harm . Performance measures include fatalities and injuries from  transportation-related incidents across all modes of transportation .
2. Jobs and commerce. How well the transportation system facilitates or supports  business development and employment . Performance measures include job creation, the movement of freight and estimates of the economic return from policies and investments .
3. Mobility. The efficient movement of people between destinations by automobile, pedestrian, bicycle and transit modes. Performance measures include congestion levels, travel times, travel speed and volume, time lost to traffic delays and on-time transit performance.
4. Access. The ability of the transportation system to connect people to desired goods, services, activities and destinations for both work and leisure, and to meet the transportation needs of different populations. Performance measures include availability and use of multimodal transportation options—including public and private transit and pedestrian and bicycle access—for the general public and populations with specific needs, such as elderly, disabled and low-income individuals .
5. Environmental stewardship. The effect of the transportation system on energy use and the natural environment . performance measures include fuel usage, transportation-related emissions, climate change indicators, and preservation of and impact on  ecological systems .
6. Infrastructure preservation. The condition of the transportation system’s assets . Performance measures include the physical condition of roads, bridges, pavements, signs, culverts and rail systems

The analysis rates weather each state considers these goals, but does not evaluate how well this is done or the degree it actually affects investment decisions. The report recommends federal, state and local policy reforms to improve government agency’s ability to evaluate investments and incorporate this information into transport planning and investment decisions.

Transit Oriented Development Performance Evaluation

The Performance-Based Transit-Oriented Development Typology Guidebook is a user-friendly tool for evaluating conditions around transit stations and determining how they influence factors such as per capita vehicle ownership and travel, consumer transportation costs, public transit ridership, energy consumption and pollution emissions (CTOD 2010). It uses real performance outcomes measured at more than 3,700 existing transit station areas in 39 regions around the United States. This information gives stakeholders the ability to evaluate the performance of the transit zones in their neighborhoods.

Renne (2009) makes the following recommendations for developing sustainable transportation performance evaluation:

1. Understand that most decisions are ultimately political – Planners need to understand that no matter how much data experts analyze, decisions are mostly made based on political factors. The importance of data is to confirm or reject assumptions that local communities make based on gut feelings. Data can assist to refine goals and objectives and ultimately create better policies to produce more sustainable outcomes.

2. Define the goals of TOD – Each community needs to define their own goals for TOD. If multiple goals exist, they should be ranked. Some communities might encourage TOD primarily from a mobility perspective while others see it as a driver of economic development. Other communities might use TOD as a way to encourage location efficient affordable housing. Without specific prioritized goals for TOD, it becomes very difficult to define success.

3. Establish baseline data across sustainability dimensions – This paper attempts to create multiple dimensions to evaluate TOD success. Baseline data is needed to track future changes to ensure that goals are not achieved at the expense of some other unintended negative externality. Collecting data from both primary (ie. the TOD Household Survey) and secondary sources (ie. census) is often necessary. Secondary sources do not provide the coverage and scope of data needed to fully evaluate TOD from a sustainability perspective. It is also important to ensure that at least some of the data collected can be compared to regional or sub-regional averages.

4. Collect data at regular intervals to track success – Once the baseline data has been established, the only way to determine success is to collect the same data, using the same methodologies, at regular intervals. Change within the TOD could be compared to change within the region (or sub-region) to determine if the TOD is becoming more or less sustainable in comparison to the average.

5. Analysis of data should include local and regional stakeholders – A mechanism needs to be established for local and regional stakeholders to discuss and debate the outcomes of the analysis. Local planners need to seek the input of the community and regional planners need to work collaboratively across agencies and layers of government to ensure political coordination.

Good Example of Bad Performance Evaluation (www.vtpi.org/per_ind.pdf)

The report, Transportation Performance of the Canadian Provinces (Hartgen, Chadwick and Fields, 2008) uses a unique set of 23 indicators to evaluate and compare transportation system performance of Canadian provinces. The report’s stated intent is to improve transportation performance nationwide by establishing key baseline information that can be used to track performance over time. The report rates provinces from best to worst with regard to specific indicators and aggregate indices.

Table 5 critiques these indicators. Although some of the study’s indicators are appropriate and commonly used, others are ambiguous, and a few are illogical for comparative analysis. For example, the safety indicator (fatality rate per billion vehicle km) and congestion indicator (annual hours of delay per capita) are widely used, but the roadway indicator (vehicle kilometers of travel per two-lane kilometer of road) is ambiguous (a higher value could indicate cost efficiency or inadequate roadway supply and congestion) and inherently favors more urbanized provinces over more rural provinces.

Table 5            Performance Index Evaluation Summary (Litman, 2008)

Indicator	Critique	Favors (Direction of Bias)	Grade
Kilometers of vehicle travel per two-lane km of road	Ambiguous. Could indicate inadequate road supply.	Urban conditions and increased vehicle traffic.	D
Provincial expenditures per major road kilometer	Inappropriate. Ignores geographic and traffic volume differences.	Rural conditions, and cheap, inferior roads.	C
Percent of major roads in fair or poor condition	Appropriate		A
Roadway travel time to Ottawa	Inappropriate. Misrepresents the concept of access.	Central provinces, particularly Ontario and Quebec.	F
Roadway travel time to US border	Inappropriate. Misrepresents the concept of access.	Southern provinces.	F
Traffic fatality rate per billion vehicle-kms	Mobility-based.	Increased motor vehicle travel.	C
Annual hours of congestion delay per capita	Appropriate, but data are limited to a few cities.	Provinces with few large cities.	B
Average round trip commuting time	Inappropriate as a road indicator; should apply to all modes.	Smaller cities and rural areas.	B
Transit ridership per capita served	Appropriate if one of several transit quality indicators.	Larger cities.	B
Transit operating cost per trip	Appropriate.	Larger cities.	B
Aviation passengers per flight	Inappropriate. Misrepresents the concept of load factor.	Cities with major airports.	D
Aviation accidents per million passengers	Appropriate.		A
Government operating cost per ferry passenger	Inappropriate. Ignores differences in costs.	Provinces with shorter and cheaper ferry services.	D
Accidents per million ferry passengers	Appropriate.		A
Tonnes of truck traffic per km of road	Ambiguous. Could indicate inadequate roads.	Urban areas and increased freight truck volumes.	D
Fatal collisions per million tonnes	Mobility-based.	Increased motor vehicle travel.	B
Total employment per truck border crossing	Inappropriate. Provides meaningless information.	Provinces with more jobs and fewer border crossings.	F
Tonnes of cargo per flight	Inappropriate. Misrepresents the concept of load factor.	Cities with major airports.	D
Origin tonnes per km of first line track	Ambiguous. Indicates little about true cost efficiency.	Provinces that generate high rail freight volumes.	C
Rail accidents per million originating tonnes	Appropriate.		A
Port operator expenditures per tonne handled	Ambiguous. Indicates little about true cost efficiency.	Provinces with cheaper-to-handle marine freight.	D
Port expense/revenue ratio	Appropriate, but fails to account for factors such as investment.	Provinces not currently improving port facilities.	B
Shipping accidents per million tonnes	Fails to account for different types of freight	Provinces with safer-to-handle marine freight.	B

This table critiques performance indicators used by Hartgen, Chadwick and Fields.

Access to Destinations (www.cts.umn.edu/access-study)

Access to Destinations is an interdisciplinary research program by the University of Minnesota’s Center for Transportation Studies which investigate how people use the transportation system, and how transportation and land use interact. The research has developed tools and data sets that quantify overall accessibility that take into account multiple transport modes (walking, cycling, public transit and automobile) and land use development patterns. This analysis indicates that accessibility is affected by both travel speed and geographic proximity. The research project initially applied this model to the Twin Cities region. It found:

· In 1995 there was only one traffic analysis zone (located near the center of the metro region) from which commuters could reach more than one million jobs within 20 minutes. By 2005, there were 20 such zones. Well over half the population of the region can reach more than one million jobs within 30 minutes, and almost everyone can reach a million jobs within 45 minutes.

· These accessibility increases occurred while the center of gravity for employment was shifting—slightly—toward the south and west of the region. Accessibility got better despite the absence of a matching shift on the part of workers. The labor force tended to shift more toward zones north and south of Minneapolis. The overall ratio of jobs to workers has been improving (getting closer to 1:1) in most areas of the region.

· The region has seen small but measurable decreases in walking travel time. Making it easier and safer to walk (for example, by adding new pedestrian facilities such as the Midtown Greenway in Minneapolis) raises walking’s desirability and reduces the time involved in a walking trip.

· A third of walking trips exceeded a mile, calling into question the long-standing assumption that a quarter-mile is the limit of willingness to walk to destinations.

· New bike networks and facilities also had a measurable effect.

· The region’s first light-rail line had a positive effect on many accessibility measures. Accessibility increases were proportionately greater along the Hiawatha light-rail corridor and near bus lines offering high-frequency service.

· Results indicate that centralized population and employment produce the highest accessibility across all networks.

Subsequent research analyzed accessibility by mode (automobile and transit) and purpose (work and non-work trips) for about 30 US metropolitan areas (Levine, et al. 2012). This analysis indicates that proximity is about ten times more influential than travel speed in determining a metropolitan area’s overall accessibility.

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.

Transport Model Performance Evaluation (Rodier and Spiller 2012)

The report, Model-based Transportation Performance: A Comparative Framework and Literature Synthesis, incorporates various performance indicators into transportation modeling in order to evaluate the effectiveness of various land-use, transit, and automobile pricing policies. The results indicate the direction and relative magnitude of change resulting from these policies, as well as potential biases that result in analyses that overlook some of these impacts. Table 6 summarizes the performance indicators used in this modeling.

Table 6            Performance Indicator Framework (Rodier and Spiller 2012)

	Performance Indicator	Required Model Data
Travel	Access	Travel time/cost by origin/destination location, mode, area (corridor, subarea, region), time of day (peak and off-peak), and/or activity type (work, school, shop)
	Proximity	Quantity of land consumed; redevelopment and/or infill by type, area, and/or location; total jobs by total households by area
	Choice	Transit, pedestrian, and bicycle mode share by area
	Congestion	Vehicle speed/distance by mode (including trucks), activity type, area (key corridors or economic destinations)
	Access	Access by socioeconomic group and location
Equity	Spatial	Clustering of socioeconomic groups by location
	Housing	Home location change attributed to rent increase by socioeconomic group
	Housing	Supply and cost (rent/own) by type and location
Economic	Financial/land use	Built-form input to service cost, tax, and/or infrastructure cost model
	Financial/transport	Use and revenue relative to capital and operation and maintenance (O&M) costs
	Surplus	Spatial economic effects (producer and consumer surplus)
Environmental	Energy/climate/air	Vehicle activity in fuel use, climate change, and emissions models
	Noise	Residential location and vehicle facilities in noise models
	Habitat/ecosystem/ water	Land consumed by type and location input to habitat, ecosystem, and water models

This table summarizes performance indicators incorporated in transport models for more comprehensive analysis of impacts of various policy and planning options.

Integrated Transit Data

The Integrated National Transit Database Analysis System (INTDAS) (www.ftis.org/intdas.html) is an Internet-based system that integrates the FTA’s National Transit Database data from multiple years into a single database with user-friendly interface and analysis tools for easy data access and analysis (Gan, Gui and Tang 2011). This is designed to facilitate trend analysis and performance evaluation that compares different agencies and jurisdictions.

Evaluating Mobility Metrics

The study, Development and Sensitivity Testing of Alternative Mobility Metrics (Gliebe and Strathman 2012) evaluated and compared the results of several roadway mobility indicators including Network-wide V/C Changes, Total Network Travel Time and Distance, Total Person Hours of Travel Time, Average Person Trip Travel Times, Trip Length Distributions, Mode Shares, Regional Accessibility to Jobs/Shopping Opportunities and Local Accessibility (20-Minute Neighborhood). The study concluded that v/c ratios are an extendable and robust evaluation metric. It recommends using a network-wide v/c budget, which considers v/c ratios across a larger area, and regional accessibility measures, as a general approach for demonstrating the benefits to a region of a land use change proposal.

Multi-Modal Level-Of-Service Indicators

The 2010 Highway Capacity Manual (TRB 2010) includes multi-modal performance indicators based on an extensive research program that developed Level-of-Service (LOS) ratings which measure how various facility design factors affects walking, cycling, automobile and public transit travel (Dowling Associates 2008). These include:

· Cycling LOS takes into account the availability of parallel bicycle paths, the number of unsignalized intersections and driveways (because they create conflicts between cyclists and other vehicles), width of outside through lane or bicycle lane (the degree of separation between bicyclists and motor vehicle traffic), motor vehicle traffic volumes and speeds, portion of heavy vehicles (large trucks and buses), the presences of parallel parked cars, grades (hills), and special conflicts such as freeway off-ramps.

· Pedestrian LOS takes into account pedestrian facility crowding, the presence of sidewalks and paths, vehicle traffic speeds and volumes, perceived separation between pedestrians and motor vehicle traffic (including barriers such as parked cars and trees), street crossing widths, extra walking required to reach crosswalks, average pedestrian crossing delay (time needed to wait for a gap in traffic or a crosswalk signal), and special conflicts such as multiple free right-turn lanes (which tend to be difficult for pedestrians to cross).

New York City Street Performance

The New York City Department of Transportation uses the following goals, strategies and performance indicators for evaluating city streets designs.

Table 7            New York City Street Performance Metrics (NYCDOT 2012)

Goals	Strategies	Metrics
Safety Accommodate all users Create great public spaces	Design safe streets Build great public spaces Improve bus service Reduce delay and speeding Efficiently manage parking and loading	Motorist, pedestrian, and cyclist crashes and injuries Vehicle, bus passenger, bicycle rider, and public space user volumes Traffic volumes Travel speeds Traffic speeds (not too slow, but not too fast) Economic vitality (retail sales, building vacancies, visitors) Bus ridership and travel speeds User satisfaction Environmental and public health quality Double parking and parking duration

New York City has established these goals, strategies and metrics for evaluating city street performance.

Sustainable Transportation Economic Evaluation (www.transpotohealthlink.com/sustainable-return-on-investment.html)

The HDR Sustainable Return on Investment (SROI) process  assesses the economic, social and envioronmental benefits of a transportation infrastructure project. It includes four phases:

I. Development of a structured and logical plan (assessment of “how” all the variables and assumptions interact to determine the impact of a project).

II. Quantifying the input data and assumptions (statistical probability/uncertainty analysis of the project elements).

III. Risk assessment session with stakeholders (step 2 elements).

IV. Model Simulation and forecasting results (data modeling of various project scenarios and statistically based probability distributions).

The SROI model promotes transparency, accountability, and efficient use of all social resources necessary to maximize the “triple bottom line” of economic, social and environmental value. In addition, the SROI methodology builds on best practices in Cost-Benefit Analysis and Financial Analysis methodologies, complemented by state-of-the-art Risk Analysis and Stakeholder Elicitation techniques.The SROI process identifies the significant impacts of a project and values these impacts in monetary terms, while accounting for non-monetary benefits and external costs and benefits.

In essence, the SROI is a feasibility study in conjunction with the monetized value of non-cash costs of environment, community variables and external benefits.Together the SROI elements determine the overall utility (full value) and risk of a project. The SROI model also represents an ecological approach that provides a “general framework for understanding the nature of people's transactions with their physical and socio-cultural surroundings. Therefore “people” are the determining factor influenced both by the physical (e.g., geography, architecture, and technology) and social environment (e.g., culture, economics, and politics). Human utility (health/well-being) is multifaceted with an interplay among all of the elements and factors of the SROI model. Interestingly, the SROI concept is the basis of public health planning and program development within the built environment.

SROI originated from a Commitment to Action to develop a new generation of public decision support metrics for the Clinton Global Initiative (CGI). SROI was developed with, and peer-reviewed, by Columbia University’s Graduate School of International Public Affairs and launched at the 2009 CGI annual meeting. The SROI process has been used by HDR to evaluate the monetary value of sustainability programs and projects valued at over $10 Billion.

New Zealand Sustainable Transport Benchmarking (Henning, et al. 2011)

The New Zealand Transport Agency is developing a comprehensive benchmarking system to evaluate transport system performance. This will be used to monitor trends and compare cities. It identified the following ten Key Performance Indicators (KPIs):

· Traffic congestion indicator

· Travel mode share

· Public transit ridership

· Number and size of Park & Ride facilities

· Passenger kilometers traveled by public transport

· Road network length

· Population and employment density

· Parking density

· Cost of travel

· Travel personal security

· Road accident fatalities and injuries

· Vehicle harmful emissions

· Vehicle fuel consumption

· Vehicle occupancy

Transportation Performance Management Website (www.fhwa.dot.gov/tpm)

One of the key features of MAP-21 is establishment of a performance- and outcome-based program. Transportation performance management (TPM) is a strategic approach that uses system information to make investment and policy decisions in support of national performance goals. The Federal Highway Administration (FHWA) is establishing measures to assess performance or condition in specific areas, including safety, pavement conditions, traffic congestion, pollution emissions, and freight movement.

Multi-Modal Performance Indicators (FDOT 2012)

The report, Expanded Transportation Performance Measures to Supplement Level of Service (LOS) for Growth Management and Transportation Impact Analysis, by researchers at the University of Florida’s Transportation Research Center, critically evalautes current transport system performance indicators such as Roadway Level of Service (LOS), which focus on traffic congestion problems and favor automobile-oriented solutions, often to the detriment of other modes. It identifies and evaluates more multi-dimensional and multi-modal transport system performance indicators. It discusses the logical connections between goals (general things a community wants to achieve), objectives (specific ways to achieve those goals), and performance measures (specific variable used to measure progress toward goals and objectives). Potential performance measures and their data requirements are identified and evaluated based on criteria including feasibility, usefulness, agency acceptability, multi-modalism, robustness and affordability. The report summarizes various examples from Florida cities that apply multi-modal transport system performance evaluation, and provides guidance for selecting and applying them in a particular situation.

Multimodal Street Performance Evaluation (Brozen, et al. 2014)

This study compared four often-cited multimodal level of service (LOS) metrics; those of the cities of Fort Collins, Colorado and Charlotte, North Carolina; metrics developed by the San Francisco Department of Public Health (BEQI/PEQI), and the multimodal LOS metrics of the 2010 Highway Capacity Manual. It explored the differences between each metric and how those affect analysis results by applying then to the same five street segments representing a variety of physical and operational characteristics. The study found that these tools can produce radically different scores for the same street segment. The analysis indicates that with segments that rate relatively good for walking and cycling the tools produced fairly similar scores, but as active transport quality decline the scores diverged. This exercise also elucidated some challenges in using the tools; including their inability to evaluate innovative or unusual infrastructure; such as a pedestrian mall. The study recommends that transportation agencies select tools that are most consistent with their goals and perspective.

The study also analyzed how sensitive each tool is to assess current conditions and evaluate proposed future changes. The researchers analyzed the projected impacts of proposed five different pedestrian and bicycle improvement scenarios for a selected street segment. The results indicate that all of the scoring mechanisms recommended a road diet scenario with a painted buffer next to a bicycle lane, but newer bicycle configurations and treatments were often difficult and sometimes impossible to evaluate using these tools. The favored pedestrian scenario differed from the favored bicycle scenario, and the results were less consistent. Overall, the results demonstrate that these tools can evaluate changes to the street and guide future improvements. However, their ability to measure the effectiveness of innovative treatments is limited.

Global Mobility Report

The Global Mobility Report 2017, produced by Sustainable Mobility for All, a coalition of major international development organizations, assesses the performance of various transport modes (road, air, waterborne, and rail transport), and the sector’s progress toward four main objectives: universal access, efficiency, safety, and green mobility. The report will be updated semi-annually.

Key Findings:

Universal Access

• Many people continue to lack access to transport. In Africa, an estimated 450 million people–more than 70% of the region’s rural population – are still unable to reach jobs, education and healthcare services due to inadequate transport.
• Transitioning to sustainable mobility would allow Africa to become food self-sufficient and create a regional food market worth $1 trillion by 2030.

Efficiency

• The main transport technologies in use today came out of the industrial revolution. Since then, the volume of car traffic has increased tenfold, while cycling and public transport have seen hardly any growth.
• When considering all transport costs—including vehicle acquisition, fuel, operational expenses, and losses due to congestion—the move toward sustainable mobility can deliver savings of $70 trillion by 2050.

Safety

• Road transport claims the bulk of fatalities worldwide: it accounts for 97% of the deaths and 93% of the costs.
• Aviation has seen a continuous reduction in the number of fatalities and fatal crashes over recent years. Some regions have even begun to experience zero fatalities.

Green Mobility

• The transport sector contributes 23% of global energy-related greenhouse gas emissions and 18% of all man-made emissions.
• The increase in cycling and e-bike use would save the world a cumulative $24 trillion between 2015 and 2050.

California Shifts from LOS to VMT (www.vta.org/los-vmt)  

California Senate Bill 743 (Steinberg, 2013) requires that, for purposes of the California Environmental Quality Act (CEQA), the impacts of transportation policy and planning decisions be evaluated based on their effects on total vehicle miles travelled (VMT) impacts, with the assumption that increased vehicle travel increases environmental impacts, so VMT reductions are an environmental goal. This replaces indicators such as roadway “level of service,” which assume that reducing vehicle delay reduces environmental impacts, which tends to justify roadway expansions that may increase total VMT. The Technical Advisory on Evaluating Transportation Impacts in CEQA, produced by the Governor’s Office of Planning and Research (GOPR 2017), provides guidance for predicting how local policies and planning decisions will affect vehicle travel and therefore the environment for CEQA analysis.

Seattle Shifts from LOS to Multi-Modal Transport Performance (https://bit.ly/2TazqEG)

In 2019, the City of Seattle established a new process for evaluating the impacts of developments and transportation system changes, which uses multi-modal level-of-service indicators, with performance targets for transportation modes (e.g., walking, biking, transit, and driving) to be achieved by 2035. A total of eight geographic sectors were identified in the city’s comprehensive plan update, each with its own target. These favor policies and projects that reduce single-vehicle travel rates and shift travel to other modes.

CalTrans Strategic Management Plan 2015-2020 (CalTrans 2015)

The CalTrans Strategic Management Plan 2015-2020 includes detailed performance indicators in the following categories:

• Safety and Health: Provide a safe transportation system for workers and users and promote health through active transportation and reduced pollution in communities. The safety of our workers and the users of California’s transportation system is our number one priority. The most important attribute of a transportation system is that it is safe for users and can be planned, designed, built, maintain and operated safely. Our transportation system has measurable effects on the health of Californians. This is manifest by the impact of emissions from the transportation system, and the health benefits of active transportation programs.
• Stewardship and Efficiency: Money counts. Responsibly manage California’s transportation-related assets. As stewards of a transportation system that is vital to the economy and livability of our state, Caltrans is committed to the most effective and efficient use of every transportation dollar. Caltrans will keep California’s transportation system in the best condition possible and advocate for adequate resources.
• Sustainability, Livability and Economy: Make long-lasting, smart mobility decisions that improve the environment, support a vibrant economy, and build communities, not sprawl. Sustainability is a central element of our new Mission. Caltrans has chosen to define sustainability as the consideration of these three areas:
• People− fostering community health and vitality,
• Planet − preserving and restoring environmental and ecological systems,
• Prosperity − promoting economic development. Over time, sustainability elements will be incorporated into all Caltrans programs, policies, processes, projects, plans, and procedures.

• System Performance: Utilize leadership, collaboration, and strategic partnerships to develop an integrated transportation system that provides reliable and accessible mobility for travelers. A transportation system must be safe, well-maintained, and high-performing. System Performance is managed on a regional and corridor basis. We must work with our partners to ensure the State’s transportation system is contributing to an efficient and interconnected network.
• Organizational Excellence: Be a national leader in delivering quality service through excellent employee performance, public communication, and accountability. A world-class transportation system requires a worldclass staff to plan, design, build, maintain, operate, and manage it. Significant achievements can, and will, be accomplished with a capable, educated, welltrained, and motivated workforce equipped with the right tools and resources. Caltrans is committed to providing its staff with these tools and resources.

Complete Streets Outputs and Outcomes (Ranahan, Lenker and Maisel 2014)

The report, Evaluating the Impact of Complete Streets Initiatives describes a framework for evaluating various outputs (e.g., miles of on-street bicycle routes, number of crosswalk enhancements, installed curb ramps) and outcomes (e.g., level of service, crash and injury data, mode share, perceived safety, citizen satisfaction) resulting from complete streets projects. Starting with a universe of more than 800 indicators, the study consolidated them into seven major categories of impact: citizen input; economic; environmental; health; safety; multi-modal level of service; and bicycle/pedestrian. Each of the seven categories is described in a section that includes: (a) a definition of the category and its importance; (b) common measurement approaches for that category; (c) novel and innovative measurement tools; and (d) strategies for measurement. The measurement tools were selected based on their potential importance, frequency of use, availability, and cost.

AARP Livability Index (https://livabilityindex.aarp.org)  

The American Association of Retired Persons (AARP) Livability Index measures housing, neighborhood, transportation, environment, health, engagement, and opportunity characteristics. For each category, the Index evaluates current conditions using a diverse set of indicators, and identifies policies and programs that can enhance community livability. The website provides Livability ratings for U.S. communities based on indicators.

Trans-Africa Project (www.uitp.org/knowledge/projects-details.cfm?id=444)

The Trans-Africa project aims to promote public transport in Sub-Saharan Africa, taking account the unique challenges in that region. It is led by a Consortium formed by the International Association of Public Transport (UITP), the African Association of Pubic Transport (UATP) and the European Union.

The Report On Statistical Indicators Of Public Transport Performance In Sub-Saharan Africa (UITP 2010) provides information urban population and area, vehicle ownership, Gross Domestic Product (GDP) per capita, portion of household budget devoted to transport, roadway supply, percentage of paved roads, number of vehicles by mode (motorcycles, cars, buses, trucks), capacity (seats) and occupancy per vehicle,  average annual kilometers per vehicle, annual passengers per transit vehicle, daily trips per transit vehicle, mode share (walking, cycling, motorcycle, private car, private taxi, public transit, informal public transit, etc.), annual roadway investments, annual investments in public annual private car operating costs, annual fuel consumption per vehicle, annual operating costs of public transit vehicles, transit, annual public transit revenue, transit fares, traffic fatalities, vehicle air pollutants, and average traffic speeds.

Ottawa Transportation Master Plan Performance Indicators (www.ottawa.ca/city_services/planningzoning/2020/transpo/toc_en.shtml)

The City of Ottawa’s Transportation Master Plan identifies the transportation facilities and services that the City will implement to serve a rapidly growing population. It supports the Ottawa 20/20 growth management strategy and the City’s Official Plan, which guides the City’s physical development. Table 8 summarizes specific performance indicators that will be used to evaluate progress toward transportation goals and objectives.

Table 8            City of Ottawa Transportation Performance Indicators

Performance Objectives	Performance Indicators	Measurement Period	Location, Source and Frequency of Measurement	Target	City Influence
1. Limit motor vehicle traffic growth
(a) Reduce motor vehicle use per capita	Individual automobile use (vehicle-km per capita)	Year	To be determined	TBD	Medium
	Relative growth in traffic volumes (% change in volumes / % change in population)	Afternoon peak period	Aggregated key screenlines (counts, annual)	Less than 1.0	Medium
(b) Increase motor vehicle occupancy rates	Auto occupancy (persons per vehicle)	Afternoon peak period	a) Aggregated key screenlines (counts, annual) b) City-wide (origindestination survey, every 10 years)	Not less than 1.3 (both screenline and city-wide)	Low
2. Increase transit use
(a) Increase transit ridership per capita	Transit passenger volumes (rides per capita)	Year	City-wide (counts, counts)	200	High
	Transit modal split (% of motorized trips)	Afternoon peak period	a) Key screenlines (counts, annual) b) City-wide (origindestination survey, every 10 years)	a) Ref. Figure 3.7 b) 30%	High
(b) Increase service availability	Proximity to employment (% of jobs within 400 m walk of 10-minute headway service in peak periods)	Morning peak period	City-wide (employment survey, every 5 years)	TBD	High
	Service level (vehicle-km per capita)	Year	City-wide (service statistics, annual)	TBD	High
(c) Increase service speed and reliability	Intersection approaches with transit signal priority (number)	N/A	City-wide (inventory, annual)	TBD	High
	Completion of transit priority network (%)	N/A	City-wide (inventory, annual)	100%	High
	Average vehicle speed (vehicle-km per vehicle-hr)	Year	City-wide (service statistics, annual)	TBD	Medium
	On-time performance (to be determined)	TBD	TBD	TBD	Medium
	Cancelled trips (% of scheduled trips)	Year	City-wide (service statistics, annual)	TBD	High
	Completion of rapid transit network (%)	N/A	City-wide (inventory, annual)	100%	High
(d) Increase user comfort and convenience	Shelter provision (% of stops)	N/A	City-wide (inventory, annual)	TBD	High
3. Increase cycling
(a) Increase cycling modal share	Cycling modal share (% of all trips)	Afternoon peak period	a) Inner Area cordon (counts, annual) b) City-wide (origindestination survey, every 10 years)	TBD (cordon) a) 3% (city-wide)	Medium
	Cycling activity index (bicycles per 100 motorized vehicles)	8 hours (morning, midday & afternoon peak periods)	Urban area (counts, biannual)	TBD	Medium
(b) Increase availability of cycling facilities	Completion of Urban Cycling Transportation Network (%)	N/A	City-wide (annual)	100%	High
4. Increase walking
(a) Increase walking modal share	Walking modal share (% of all trips)	Afternoon peak period	a) Central Area cordon (counts, annual) b) City-wide (OD survey, every 10 years)	b) TBD (cordon) c) 10% (city-wide)	Medium
(b) Increase availability of walking facilities	Sidewalk coverage (% of arterial and collector roads with sidewalks or pathways on both sides)	N/A	Urban + villages (annual)	TBD	High
5. Reduce unwanted social and environmental effects
(a) Reduce air emissions from transportation	Greenhouse gas emissions from passenger travel (kg per capita)	Year	City-wide (annual)	TBD	Medium
	NOx emissions from passenger travel (kg per capita)	Year	City-wide (annual)	TBD	Low to medium
(b) Reduce road salt use	Road salt usage (tonnes)	Year	City-wide (annual)	N/A	High
(c) Reduce road surface per capita	Road surface area (square metres per capita)	N/A	City-wide (annual)	N/A	Medium to high
6. Optimize use of existing system
(a) Increase capacity	Transportation system management coverage (% of arterial road traffic signals with real-time optimization measures)	N/A	City-wide (annual)	TBD	High
(b) Increase transit efficiency	Transit efficiency (passenger-km per vehicle-km)	Year	City-wide (annual)	N/A	Medium to high
(c) Spread peak travel demands - roads	Peak period factor for roads (% of daily person-trips in a.m. + p.m. peak periods)	N/A	Aggregated key screenlines (counts, annual)	N/A	Low to medium
(d) Spread peak travel demands - transit	Peak period factor for transit (% of daily person-trips in a.m. + p.m. peak periods)	N/A	Aggregated key screenlines (counts, annual)	N/A	Low to medium
7. Manage transportation assets
(a) Maintain adequate condition of road, Transitway and structures	Major infrastructure condition (% of road, Transitway and structure lane-km meeting or exceeding Performance Indicator Acceptability Benchmarks)	N/A	City-wide (annual)	100%	High
(b) Maintain adequate condition of walking and cycling infrastructure	Walking and cycling infrastructure condition (% of sidewalk and cycling network meeting or exceeding Performance Indicator Acceptability Benchmarks)	N/A	City-wide (annual)	100%	High
(c) Maintain adequate condition of transit fleet	Average vehicle age (years)	N/A	City-wide (annual)	9 yr	High
8. Improve transportation safety
(a) Reduce death and injury from collisions	Road injuries (number)	Year	City-wide (annual)	30% reduction by 2010	Medium
	Road fatalities (number)	Year	City-wide (annual)	30% reduction by 2010	Medium
(b) Increase walking safety	Reported pedestrian collisions (number)	Year	City-wide (annual)	30% reduction by 2010	Medium
(c) Increase cycling safety	Reported cyclist collisions (number)	Year	City-wide (annual)	30% reduction by 2010	Medium
9. Enable efficient goods movement
(a) Minimize delay for trucks	Off-peak road congestion (volume/capacity)	Mid-day period	At aggregated key screenlines (annual, counts)	TBD	Medium
10. Meet mobility needs of persons with disabilities
(a) Increase accessibility of conventional transit service	Bus accessibility (% of lowfloor buses in fleet)	N/A	City-wide (annual)	100% by 2015	High
	Access to information (% of transit schedule information that is accessible on Web site)	N/A	Annual	TBD	High
(b) Maintain adequate specialized transit service	Usage (eligible passenger trips per capita)	Year	City-wide (annual)	TBD	High
(c) Increase accessibility of public rights-of-way	Pedestrian crossing accessibility (% with depressed curbs)	N/A	City-wide (annual)	TBD	High
	Traffic signal accessibility (% with accessibility features)	N/A	City-wide (annual)	TBD	High
	Traffic signage accessibility (to be determined)	TBD	TBD	TBD	High
11. Meet public expectations
(a) Increase satisfaction with transportation system	Public satisfaction with transportation system (% people rating as good or better) Overall Walking Cycling Transit General traffic	N/A	City-wide (annual)	100%	Medium
(b) Ensure transportation funding that is adequate and equitable	Capital investment (dollars per capita in municipal transportation projects) Roads (multimodal) Transit facilities and fleet Walking facilities Cycling facilities	Year	City-wide (annual)	N/A	High
	Operating investment (dollars per capita in municipal transportation projects) Roads (multimodal, including walking and cycling) Transit
	Reliance on property tax (% of capital investment derived from property tax rather than more equitable sources) Roads (multimodal) Transit facilities and fleet Walking facilities Cycling facilities	Year	City-wide (annual)	TBD	Low

This table summarizes performance indicators used to evaluate transport system quality in Ottawa, Canada.

References and Resources for More Information

AllTransit (http://alltransit.cnt.org) is a multi-facetted transit performance index system that provides quantitative data on transit connectivity, access, and frequency for 805 U.S. transit agencies. This information can be used transit service and transit-oriented development evaluation and planning.

Philip Barham, Samantha Jones and Maja van der Voet (2012), State Of The Art of Urban Mobility Assessment, Quality Management tool for Urban Energy Efficient Sustainable Transport (QUEST), Executive Agency for Competitiveness and Innovation, European Commission (www.quest-project.eu/files/7/quest-state-of-the-art-of-urban-mobility-assessment.pdf).

BikeScore (www.walkscore.com/bike-score-methodology.shtml) evaluates local walking conditions on a scale from 0 - 100 based on four equally weighted components, bike lanes, hills, destinations and road connectivity and bike commuting mode share.

Madeline Brozen, et al. (2014), Exploration and Implications of Multimodal Street Performance Metrics: What’s A Passing Grade? UCTC-FR-2014-09, University of California Transportation Center (www.lewis.ucla.edu); at http://bit.ly/1DlsKax.

Eric Christian Bruun (2014), Better Public Transit Systems: Analyzing Investments and Performance, Earthscan (www.routledge.com/sustainability).

CAI-Asia (2011), Walkability in Indian Cities, the Clean Air Initiative for Asian Cities (www.cleanairinitiative.org); at http://bit.ly/2tDYbf3.  

CalTrans (2015), CalTrans Strategic Management Plan 2015-2020, California Department of Transportation (www.dot.ca.gov); at www.dot.ca.gov/perf/library/pdf/Caltrans_Strategic_Mgmt_Plan_033015.pdf.

Cambridge Systematics (2010), Measuring Transportation Network Performance, NCHRP 664, TRB (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_664.pdf.

CTE (2008), Improved Methods For Assessing Social, Cultural, And Economic Effects Of Transportation Projects, NCHRP Project 08-36, TRB (www.trb.org) and AASHTO; at www.statewideplanning.org/_resources/234_NCHRP-8-36-66.pdf.

CTOD (2010), Performance-Based Transit-Oriented Development Typology Guidebook, Center for Transit-Oriented Development (www.reconnectingamerica.org); at www.cnt.org/publications/performance-based-transit-oriented-development-typology-guidebook.  

CTS (2010), Measuring What Matters: Access to Destinations, the second research summary from the Access to Destinations Study, Center for Transportation Studies, University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/pdfdownload.pl?id=1426.

Juan De Oña, et al. (2016), “A Composite Index for Evaluating Transit Service Quality Across Different User Profiles,” Journal of Public Transportation, Vo. 19, No. 2, pp. 128-153 (DOI: http://dx.doi.org/10.5038/2375-0901.19.2.8); at http://scholarcommons.usf.edu/jpt/vol19/iss2/8.

Michelle DeRobertis, et al. (2014), “Changing the Paradigm of Traffic Impact Studies: How Typical Traffic Studies Inhibit Sustainable Transportation,” ITE Journal (www.ite.org), May, pp. 30-35.

DfT (various years), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport (www.webtag.org.uk/index.htm). This website provides comprehensive guidance on how to identify problems, establish objectives, develop potential solutions, create a transport model for the appraisal of the alternative solutions, how to model highway and public transport, and how to conduct economic appraisal studies that meet DoT requirements.

Chhavi Dhinghi (2011), Measuring Public Transport Performance- Lessons for Developing Cities: Sustainable Transport Sourcebook, Sustainable Urban Transport Project (www.sutp.org) Asia and GIZ; at www.sutp.org/index.php?option=com_content&task=view&id=2826.

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. This describes ways to evaluate roadway design impacts on various modes (walking, cycling, driving and public transit).

Dowling (2010), CompleteStreets LOS: 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.

EDRG (2010), Interactions Between Transportation Capacity, Economic Systems, and Land Use Merged with Integrating Economic Considerations in Project Development, Strategic Highway Research Program (SHRP 2) Report S2-C03, Transportation Research Board (www.trb.org); at http://144.171.11.40/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=2162.

Lily Elefteriadou, Richard Dowling and Paul Ryus (2015), “Exploring Multimodal Analysis in the Highway Capacity Manual 2010,” ITD Journal, Vol. 85, No. 2, pp. 27-31; at http://digitaleditions.sheridan.com/publication/?i=245375.

FDOT (2012), Expanded Transportation Performance Measures to Supplement Level of Service (LOS) for Growth Management and Transportation Impact Analysis, Florida Department of Transportation (www.dot.state.fl.us); at www.dot.state.fl.us/research-center/Completed_Proj/Summary_PL/FDOT_BDK77_977-14_rpt.pdf.

FHWA (periodical), Transportation Performance Management Digest, Federal Highway Administration (www.fhwa.dot.gov); at www.fhwa.dot.gov/tpm/resources/digest.

Albert Gan, Feng Gui and Li Tang (2011), “System for Transit Performance Analysis Using the National Transit Database,” Journal of Public Transportation, Vol. 14, No. 3, pp. 87-108; at www.nctr.usf.edu/wp-content/uploads/2011/10/JPT14.3.pdf.

John P. Gliebe and James G. Strathman (2012), Development and Sensitivity Testing of Alternative Mobility Metrics, Portland State University for the Oregon Department of Transportation (www.oregon.gov); at https://pdxscholar.library.pdx.edu/usp_fac/139.

Michael Grant, et al. (2011), Congestion Management Process: A Guidebook, DTFH61-09-Q-00083, Federal Highway Administration (www.fhwa.dot.gov); at www.fhwa.dot.gov/planning/congestion_management_process/cmp_guidebook/cmpguidebk.pdf.

Michael Grant, et al. (2014), A Performance-Based Approach to Addressing Greenhouse Gas Emissions through Transportation Planning, FHWA-HEP-14-020, Federal Highway Administration (www.fhwa.dot.gov); at http://tinyurl.com/ku7odw4.

David Green and Ian Espada (2015), Level of Service Metrics (for Network Operations Planning), Austroads (www.austroads.com.au); at www.onlinepublications.austroads.com.au/items/AP-R475-15.

Greenroads (www.greenroads.org) is a sustainability rating system for roadway design and construction, suitable new, reconstruction and rehabilitation and bridge projects. It is a collection of sustainability best practices, called credits. Achieving credits earns points toward an overall project score that indicates the roadway’s sustainability.

GOPR (2017), Technical Advisory on Evaluating Transportation Impacts in CEQA, Governor’s Office of Planning and Research (http://opr.ca.gov); at http://opr.ca.gov/docs/20171127_Transportation_Analysis_TA_Nov_2017.pdf.

Henrik Gudmundsson (2001), Indicators and Performance Measures for Transportation, Environment and Sustainability in North America, National Environmental Research Institute (www.dmu.dk/1_viden/2_Publikationer/3_arbrapporter/default.asp).

Martin Guttenplan and Seleta Reynolds (2012), “Measuring Multimodal Mobility with the Highway Capacity Manual 2010 and Other New Analysis Tools,” TR News 280, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/trnews/trnews280toc.pdf.

David T. Hartgen, Claire G. Chadwick and M. Gregory Fields (2008), Transportation Performance of the Canadian Provinces, Fraser Institute (www.fraserinstitute.org); at www.fraserinstitute.org/researchandpublications/publications/6266.aspx.

HDR (2012), Sustainable Return on Investment (SROI) Model, HDR Decision Economics (www.hdrinc.com); at www.transpotohealthlink.com/sustainable-return-on-investment.html.

Theunis F. P. Henning, Sugandree Muruvan, Wanhua A. Feng and Roger C.Dunn (2011), “The Development of a Benchmarking Tool for Monitoring Progress Towards Sustainable Transportation In New Zealand,” Transport Policy, Vol. 18, pp. 480–488 (www.sciencedirect.com/science/article/pii/S0967070X10001368).

Robert A. Johnston (2008), “Indicators for Sustainable Transportation Planning,” Transportation Research Record 2067, Transportation Research Board (www.trb.org), pp. 146 – 154; at http://pubs.its.ucdavis.edu/publication_detail.php?id=1260.

Matthew Karlaftis and Konstantinos Kepaptsoglou (2012), Performance Measurement in the Road Sector: A Cross-Country Review of Experience, International Transport Forum (www.internationaltransportforum.org): at http://internationaltransportforum.org/jtrc/DiscussionPapers/DP201210.pdf.

Kittleson & Associates (2017), Transit Capacity and Quality of Service Manual, Report 165, Transit Cooperative Research Program, Transportation Research Board (www.trb.org); at www.trb.org/Main/Blurbs/169437.aspx.

Kevin J. Krizek, et al (2009), Access to Destinations: Application of Accessibility Measures for Non-Auto Travel Modes, Center for Transportation Studies, University of Minnesota (www.cts.umn.edu); at www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=1808.

Jonathan Levine, Joe Grengs, Qingyun Shen and Qing Shen (2012), “Does Accessibility Require Density or Speed?” Journal of the American Planning Association, Vol. 78, No. 2, pp. 157-172, http://dx.doi.org/10.1080/01944363.2012.677119; at www.connectnorwalk.com/wp-content/uploads/JAPA-article-mobility-vs-proximity.pdf. Additional research results at Metropolitan Accessibility: Comparative Indicators for Policy Reform, at www.umich.edu/~umaccess/index.html.

David Levinson and Ahmed El-Geneidy (2006), Development of Accessibility Measures, Report No. 1 in the Series: Access to Destinations (Mn/DOT 2006-16), University of Minnesota Center for Transportation Studies (www.cts.umn.edu/access-study/publications).

Todd Litman (2003), “Measuring Transportation: Traffic, Mobility and Accessibility,” ITE Journal (www.ite.org), Vol. 73, No. 10, October, pp. 28-32; at www.vtpi.org/measure.pdf.

Todd Litman (2005), Well Measured: Developing Indicators for Comprehensive and Sustainable Transport Planning, VTPI (www.vtpi.org); at www.vtpi.org/wellmeas.pdf; summarized in “Developing Indicators For Comprehensive And Sustainable Transport Planning,” Transportation Research Record 2017, TRB (www.trb.org), 2007, pp. 10-15.

Todd Litman (2008), A Good Example of Bad Transportation Performance Evaluation, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/per_ind.pdf.

Todd Litman (2008), Introduction to Multi-Modal Transport Planning, VTPI (www.vtpi.org); at www.vtpi.org/multimodal_planning.pdf.

Todd Litman (2007), “Developing Indicators For Comprehensive And Sustainable Transport Planning,” Transportation Research Record 2017, Transportation Research Board (www.trb.org), pp. 10-15; at www.vtpi.org/sus_tran_ind.pdf.

Todd Litman and Tom Rickert (2005), Evaluating Public Transit Accessibility: ‘Inclusive Design’ Performance Indicators For Public Transportation In Developing Countries, Victoria Transport Policy Institute (www.vtpi.org); at www.vtpi.org/tranacc.pdf.

Michae l J. Markow (2012), Engineering Economic Analysis Practices for Highway Investment, NCHRP Synthesis 424, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_syn_424.pdf.

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

Maaza C. Mekuria, Bruce Appleyard and Hilary Nixon (2017), Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility, Mineta Transportation Institute (http://transweb.sjsu.edu); at http://transweb.sjsu.edu/PDFs/research/1205-improving-livability-using-green-and-active-modes.pdf.

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.

NARC (2012), Livability Literature Review: A Synthesis Of Current Practice, National Association of Regional Councils (www.narc.org) and the U.S. Department of Transportation; at http://narc.org/wp-content/uploads/Livability-Report-FINAL.pdf.

NACTO (2018), Making Transit Count: Performance Measures that Move Transit Projects Forward, National Association of City Transportation Officials (https://nacto.org); at https://nacto.org/wp-content/uploads/2018/04/NACTO-Making-Transit-Count.pdf. 

NYCDOT (2012), Measuring the Street: New Metrics for 21st Century Streets, New York City Department of Transportation (www.nyc.gov/html/dot); at www.nyc.gov/html/dot/downloads/pdf/2012-10-measuring-the-street.pdf.

New Zealand Transportation Agency Post Implementation Reviews (PIRs) (www.nzta.govt.nz/planning/monitoring/audits/pir.html) are conducted every year on a small sample of completed NZTA-funded projects. They allow the agency to compare the planned benefits and costs of a project with the actual outcomes achieved.

Operations Performance Measurement (www.ops.fhwa.dot.gov/perf_measurement/index.htm) is a website maintained by the U.S. Federal Highway Administration’s Office of Operations.

PCT (2011), Measuring Transportation Investments: The Road to Results, Pew Charitable Trusts and The Rockefeller Foundation (www.pewtrusts.org); at https://bit.ly/2Y47Lbr.

Performance Measures Website (http://shrp2webtool.camsys.com) helps users select performance measures for major highway capacity project evaluation. 

QUEST (Quality management tool for Urban Energy efficient Sustainable Transport) (www.quest-project.eu) is a European Commission project to assist small and mid-sized cities in improving planning for sustainable urban mobility. QUEST is developing tools for evaluating urban mobility policies based on the concept of Total Quality Management (TQM).

Molly E. Ranahan, James A. Lenker and Jordana L. Maisel (2014), Evaluating the Impact of Complete Streets Initiatives, Center for Inclusive Design & Environmental Access, University at Buffalo School of Architecture and Planning (www.idea.ap.buffalo.edu); at http://udeworld.com/documents/pdfs/IDeACenter_GoBike_CompleteStreets_web.pdf.

John Renne (2009), “Evaluating Transit-Oriented Development Using a Sustainability Framework: Lessons from Perth’s Network City,” in Planning Sustainable Communities, Sasha Tsenkova, ed., University of Calgary: Cities, Policy & and Planning Research Series, pp. 115-148; at www.vtpi.org/renne_tod.pdf.

Kerstin Robertson, Annika K. Jägerbrand and Georg F. Tschan (2015), Evaluation of Transport Interventions In Developing Countries, Report 855A, VTI (www.vti.se); at www.vti.se/en/publications/pdf/evaluation-of-transport-interventions-in-developing-countries.pdf.  

Caroline Rodier and Margot Spiller (2012), Model-based Transportation Performance: A Comparative Framework and Literature Synthesis, Report 11-09, Mineta Transportation Institute (www.transweb.sjsu.edu); at www.transweb.sjsu.edu/PDFs/research/2805-Model-based-transportation-performance.pdf.

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.

Angie Schmitt (2016), A Better Way to Track How Well Transit Performs, Street Blog Network (www.streetsblog.net); at www.streetsblog.net/2016/05/18/a-better-way-to-track-how-well-transit-performs.

Eric Schreffler (2000), State of the Practice: Mobility Management Monitoring and Evaluation in the United States, MOST: Mobility Management Strategies for the Next Decades; Work Package 3, D3 Report, Appendix C (http://mo.st/public/reports/me_usa.zip).

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.

SFCTA (2008), Draft Final Report on the Automobile Trip Generation (ATG) Impact Measure and on the Proposed ATG Transportation Impact Mitigation Fee Nexus Study, San Francisco County Transportation Authority (www.sfcta.org); at www.sfcta.org/images/stories/Executive/Meetings/pnp/2008/09sept09/atg%20memo%20pnp.pdf.

STI (2008), Sustainable Transportation Indicators: A Recommended Program to Define a Standard Set of Indicators for Sustainable Transportation Planning, Sustainable Transportation Indicators Subcommittee (ADD40 [1]), TRB (www.trb.org); at www.vtpi.org/sustain/sti.pdf.

Brad Strader (2012), “Performance Metrics for Plans, Projects, and Planners,” ITE Journal (www.ite.org), Vol. 82/1, pp. 31-32; www.ite.org/membersonly/itejournal/pdf/2012/JB12AA31.pdf

SUM4all (2017), Global Mobility Report 2017, Sustainable Mobility for All (http://sum4all.org); at http://sum4all.org/publications/global-mobility-report-2017.

Sustainable Highways Self-Evaluation Tool (www.sustainablehighways.org) by the U.S. Federal Highway Administration identifies characteristics of sustainable highways and provides procedures and techniques to help organizations apply sustainability best practices to roadway projects and programs.

Transportation Performance Management Website (www.fhwa.dot.gov/tpm), by the U.S. Federal Highway Administration.

Transportation for Communities - Advancing Projects through Partnerships (www.transportationforcommunities.com) is a decision support tool which provides technical information to help develop, prioritize, and inform transportation plans and projects.

TRB (2008), Performance Measurement Practice (www.trb-performancemeasurement.org), Performance Measurement Committee (ABC30), Transportation Research Board.

TRB (2010), Highway Capacity Manual, Transportation Research Board (www.trb.org); at www.trb.org/Main/Blurbs/Highway_Capacity_Manual_2010_164718.aspx.

TRB (2012), Sustainable Practices, Performance Measures, and Management, Transportation Research Record 2271, Transportation Research Board  (www.trb.org); at www.trb.org/Main/Blurbs/167641.aspx.

Tools for Measuring Public Life (https://gehlinstitute.org/public-life-tools) by the Gehl Institute help measure how people use public spaces and better understand the relationships between those spaces and activities that take place in them.

UITP (2010), Report on Statistical Indicators of Public Transport Performance in Sub-Saharan Africa, International Association of Public Transport (www.uitp.org); www.uitp.org/knowledge/projects-details.cfm?id=444.

UNECE (2011), Transport for Sustainable Development In The ECE Region, United Nations Economic Commission for Europe (www.unece.org); at http://live.unece.org/fileadmin/DAM/trans/publications/Transport_for_sustainable_development_in_the_ECE_region.pdf.

UN Habitat (2016), Urbanization And Development; Emerging Futures, World Cities Report 2016, United Nations Human Settlements Programme (http://wcr.unhabitat.org); at http://wcr.unhabitat.org/wp-content/uploads/sites/16/2016/05/WCR-%20Full-Report-2016.pdf.

USEPA (2011), Guide To Sustainable Transportation Performance Measures, U.S. Environmental Protection Agency (www.epa.gov); at www.epa.gov/smartgrowth/pdf/Sustainable_Transpo_Performance.pdf.

Anita Vandervalk (2018), Analyzing Data for Measuring Transportation Performance by State DOTs and MPOs: A Synthesis of Highway Practice, Synthesis 528, National Cooperative Highway Research Program (NCHRP)

Walkability Tools Research Webpage (www.levelofservice.com) provides guidance and data for Community Street Review (CSR) analysis of walkability.

Glen Weisbrod, Teresa Lynch and Michael Meyer (2007), Monetary Valuation Per Dollar Of Investment In Different Performance Measures, American Association of State Highway and Transportation Officials, NCHRP Project 08-36, Task 61, National Cooperative Highway Research Program, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/docs/NCHRP08-36(61)_FR.pdf.

Wilbur Smith (2008), Traffic & Transportation Policies and Strategies in Urban Areas in India, Ministry of Urban Development (www.urbanindia.nic.in); at http://urbanindia.nic.in/moud/programme/ut/Traffic_transportation.pdf.

WSDOT (2008), Performance Measurement Library, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/Accountability/Publications/Library.htm.

WSDOT (2011), Ten Years of Transparency: The Role of Performance Reporting at WSDOT, Washington State Department of Transportation (www.wsdot.wa.gov); at www.wsdot.wa.gov/NR/rdonlyres/A9CEFE19-BCDF-4F86-8025-3AD63BBFA1C9/0/10_Years_Transparency_performance_reporting_folio_2011web.pdf.

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