Performance Evaluation
Practical Indicators For Evaluating Progress Toward Planning Objectives
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Victoria Transport Policy Institute
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Updated 22 January 2010
This chapter describes Performance Evaluation, which applies specific performance indicators to measure progress toward specific goals and objectives.
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:
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:
Performance indicators must be carefully selected to accurately reflect goals and identify problems. Inappropriate or incomplete indicators can misdiagnose problems and misdirect decision-makers. 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 transport indicators mostly consider motor vehicles traffic conditions. Below are examples.
· 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 only consider motor vehicle travel conditions, evaluating a transportation system based on these factors tends to favor automobile-oriented improvements over other objectives and solutions. 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.
More comprehensive performance indices are important for multi-modal, Transportation Demand Management, 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.
· 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 split - Portion of travel made by walking, cycling, rideshare, public transit and telework.
· 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.
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.
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 split – 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.
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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.
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.
Transportation professionals have developed guidance for selecting indicators for transportation program evaluation (CalTrans, 2008; TRB, 2008), strategic planning (CTE, 2008), and sustainable transport planning (CST, 2003; Gudmundsson, 2001; Litman, 2005; STI, 2008). The following principles should be applied when selecting transportation performance indicators (Hart, 1997; Marsden, Kelly and Snell, 2006):
· 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.
|
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.
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.
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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?” |
CalTrans (2008), TRB (2008), CTE (2008) and Litman (2005) provide numerous examples of transportation performance indicators.
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).
Renne (2009) makes the following recommendations for developing sustainable transportation proformance 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.
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 3 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 3 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.
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 4 summarizes specific performance indicators that will be used to evaluate progress toward transportation goals and objectives.
Table 4 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.
CalTrans (2008), Transportation System Performance Measures (TSPM), California Department of Transportation (www.dot.ca.gov/hq/tsip/index.php).
Cambridge Systematics (1998), Mulitmodal Corridor Capacity Analysis Manual, NCHRP Report 399, Transportation Research Board (www.trb.org); at http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_399.pdf. Chapter 6 contains guidelines for developing performance indicators.
Cambridge Systematics (2009), Performance Measurement Framework for Highway Capacity Decision Making, Strategic Highway Research Program (SHRP) Report S2-C02-RR, TRB (www.trb.org); at http://sites.google.com/site/shrpc01.
Susan Chapman and Doug Weir (2008), Accessibility Planning Methods, Research Report 363, New Zealand Transportation Agency (www.landtransport.govt.nz/research/reports/363.pdf).
CST (2003), Sustainable Transportation Performance Indicators, Centre for Sustainable Transportation (www.cstctd.org); at http://cst.uwinnipeg.ca/completed.html.
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.
DfT (2006), 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.
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.
FHWA (2000), Transportation Performance Measures Toolbox, Federal Highway Administration (www.ops.fhwa.dot.gov/travel/deployment_task_force/perf_measures.htm).
FHWA and FTA (2002), “Establishing Meaningful Performance Measures for Benefits and Burden Assessments,” Transportation & Environmental Justice: Effective Practices, Federal Highway Administration, Federal Transit Administration, FHWA-EP-02-016 (www.fhwa.dot.gov/environment/ej2.htm).
David J. Forkenbrock and Glen E. Weisbrod (2001), Guidebook for Assessing the Social and Economic Effects of Transportation Projects, NCHRP Report 456, Transportation Research Board, National Academy Press (www.trb.org).
GPI (2008), The GPI Transportation Accounts: Sustainable Transportation in Halifax Regional Municipality, GPI Atlantic (www.gpiatlantic.org); at www.gpiatlantic.org/pdf/transportation/hrmtransportation.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).
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.
Kittleson & Associates (2003), Guidebook for Developing a Transit Performance-Measurement System, TCRP Web Document 88, Transportation Research Board (www.trb.org); at http://gulliver.trb.org/publications/tcrp/tcrp_report_88/intro.pdf.
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).
Waldo Lopez-Aqueres (1994), “Employer Trip Reduction Programs: How Costly? Who Pays?,” TDM Review, Association for Commuter Transportation (http://tmi.cob.fsu.edu/act/act.htm).
Waldo Lopez-Aqueres (1995), “Conceptual Framework to Study the Effectiveness of Employer Trip Reduction Programs,” Transportation Research Record 1404, TRB (www.trb.org), pp. 55-63.
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 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.
Measuring Walking (www.measuring-walking.org) describes internationally standardised monitoring methods of walking and public space.
Michael Meyer (2001), Measuring System Performance: The Key to Establishing Operations as a Core Agency Mission, National Dialogue on Transportation Operations
(www.ops.fhwa.dot.gov/Speech%20Files/FHWAPerformancemeasures.doc).
Ali Modarres (1993), “Evaluating Employer-Based Transportation Demand Management Programs,” Transportation Research Record A, Vol. 27, No. 4, 1993, pp. 291-297.
OTM (2008), Transportation Performance Measures, Office of Transportation Management, Federal Highway Administration (www.fhwa.dot.gov/planning/toolbox).
Performance Measurement Exchange (http://knowledge.fhwa.dot.gov/cops/pm.nsf/home), is a website supported by FHWA and TRB to promote better transportation decision-making.
Tara Ramani (2009), Developing Sustainable Transportation Performance Measures for TxDOT’s Strategic Plan – Technical Report, Texas Transportation Institute (http://tti.tamu.edu) for the Texas Department of Transportation; at http://tti.tamu.edu/documents/0-5541-1.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.
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).
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.
TRB (2008), Performance Measurement Practice (www.trb-performancemeasurement.org), Performance Measurement Committee (ABC30), Transportation Research Board.
USEPA (2008), Indicator Development for Estuaries Manual, U.S. Environmental Protection Agency (www.usepa.gov); at www.epa.gov/owow/estuaries/indicators/pdf/indicators_manual.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.
This Encyclopedia is produced by the Victoria Transport Policy Institute to help improve understanding of Transportation Demand Management. It is an ongoing project. Please send us your comments and suggestions for improvement.
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