Transportation Demand

Evaluating The Amount And Type Of Travel People Would Choose Under Specific Conditions


TDM Encyclopedia

Victoria Transport Policy Institute


Updated 29 May 2015

This chapter discusses concepts related to Transport Demand, which refers to the amount and type of travel people would choose under specific price and service quality conditions.




Transportation Demand refers to the amount and type of travel people would choose under specific conditions, taking account factors such as the quality of transport options available and their prices. Understanding demand is important for Transport Planning in general and is particularly important Transportation Demand Management, which includes various strategies that influence travel behavior.


Many factors can affect travel demand, including demographics, the quality of facilities, the quality and price of alternatives, and land use patterns, as summarized in Table 1. Changes to these factors, due to trends or by design, can affect travel activity and therefore costs and problems such as congestion, accidents and pollution emissions.


Table 1            Factors That Affect Transport Demand




Transport Options

Service Quality

Land Use

Number of people (residents, employees and visitors).









Number of jobs




Business activity


Freight transport


Tourist activity


Fuel prices and taxes


Vehicle taxes & fees


Road tolls


Parking fees


Vehicle insurance


Public transport fares





Public transit






Taxi services




Delivery services

Relative speed and delay






Safety and security


Waiting conditions


Parking conditions


User information


Social status









Transit service proximity


Roadway design


This table indicates various factors that affect transport demand, which should be considered in transport planning and modeling, and can be used to manage demand.



For example, rather than saying that “Vehicle travel is projected to increase by 20% during the next decade” it would be more accurate to say that “Vehicle travel is projected to increase by 20% during the next decade if current trends continue, or 10% if the community invests more in alternative modes, or not at all if the community also implements smart growth land use policies, and is projected to decline 10% if additional TDM strategies are implemented, such as efficient road and parking pricing.” This highlights the point that current policy and planning decisions can affect future travel activity.


Automobile, rail transit and air travel demand tend to increase with incomes, particularly as household shift from low to medium incomes. At high incomes, automobile travel tends to level off, and the point at which it peaks depend on factors such as the quality of alternative modes and land use policies. For example, U.S. consumers drive about twice as much in the U.S. as in other wealthy countries (Litman 2006b; Ecola, et al. 2014). Demand for walking, cycling and bus transit varies significantly depending on quality: these modes are often used by lower-income travelers and so are considered inferior goods, but in some situations they are also used significantly by higher-income travelers (APTA 2007). For example, high income neighborhoods often have high walking and cycling mode shares, and some express commuter bus services carry relatively higher-income commuters.


Table 2            Demand Characteristics By Transit Mode (CTS 2009)

Transit Service


Type of Rider

How Transit is Accessed

Trip Characteristics



Hiawatha Line from downtown

Minneapolis to its

southern suburbs

Mostly (62%) choice

Balanced between bus, walking, and park and ride


Home locations spread throughout the region; the average rider lives more than three miles from the line.




Connects suburban areas directly to downtowns

Primarily choice (84%)


About half park-and-ride (48%)


Home locations clustered at the line origin





Express routes with coach buses


Almost exclusively choice (96%)


Mostly park and ride (62%)


Home locations clustered at the line origin

Local Bus

Serves urban and suburban areas with frequent stops

Mostly captive (52%)


Nearly all bus or walk (90%)


Home locations scattered along route; most riders live within a mile of the bus line

Rail transit tends to attract more “choice” riders (discretionary transit users who could drive).



Described differently, as incomes increase consumers tend to be more sensitive to service quality than price, so higher quality services are justified to meet consumer demands. This can include a diverse range of factors including the ease of accessing information; walking and waiting conditions; station and vehicle cleanliness and comfort; amenities such as the availability of washrooms, refreshments and Internet connections; and the respect shown customers during interactions with service providers. For example, to attract higher income travelers, public transit services should be modeled after first-class rather than economy airline travel.


Similarly, as incomes increase, demand for non-transportation goods also tends to increase, including some that are in conflict with motor vehicle travel, such as reduced commuting time and stress, environmental quality (including both the built environment – towns and cities – and the natural environment), health and safety. As a result, as people become wealthier, many choose to live in more accessible, multi-modal communities so they can reduce their vehicle travel and rely more on alternative modes.


An important planning issue is the degree to which the transport system responds to consumer demands. For example, high automobile travel mode split may results from:


·         Automobile travel superior performance. Consumers have viable options (they could walk, bicycle and use public transit) but prefer driving for most trips.


·         Automobile travel prestige. Consumers have viable options but are often embarrassed to use them, and so choose driving for most trips.


·         Inadequate alternatives. Distorted planning practices have reduced the quantity and quality of alternative modes, so walking, cycling and public transit are unavailable even when they are more cost effective than existing alternatives or consumers would willingly pay marginal costs.


·         Miss-pricing. Since most vehicle costs are fixed or external, once consumers purchase an automobile they feel that they should use it, in order to get their money’s worth. As a result, consumers drive more and use alternatives less than is optional overall. Described differently, consumers lack efficient pricing options, such as unbundled parking and distance-based insurance.



It is likely that all four factors contribute to high levels of automobile travel in some situations. To the degree that automobile travel offers true superior performance, an automobile-dependent transportation system responds to consumer demands. However, to the degree that other factors (prestige, inadequate alternatives, mis-pricing) contribute to high automobile travel mode split, the resulting travel patterns are not optimal; consumers are forced to drive more than they actually want and are unable to use preferred alternatives, due in part to inadequate options.


The following trends are expected to change future travel demands (Litman 2006b; Ecola, et al. 2014):



These trends tend to reduce motor vehicle travel demand (or at least, growth in demand) and increase demand for alternative modes. This is not to suggest that automobile travel will disappear, but at the margin (compared with current travel patterns) many people would probably prefer to drive somewhat less and rely more on alternative modes, provided that they are convenient, comfortable, safe and affordable. Exactly how much travel behavior actually changes depend on factors such as the quality of alternative modes, Pricing and Land Use Factors. Described differently, as people become wealthier and their basic mobility needs are satisfied they become increasingly responsive to qualitative factors such as comfort and health concerns, and will choose transport options and home locations that respond to their preferences. For example, many wealthy people now choose homes in urban neighborhoods because they prefer being able to walk, bike and use high quality public transit for a portion of trips, rather than relying entirely on automobile transport. These are all aspects of transport demand.


Travel Demand Models can be used to predict how these factors affect travel behavior. Conventional models tend to focus on certain factors (demographics, vehicle operating costs, travel speeds, public transit fares) and overlook other factors, particularly qualitative factors related to travel comfort and land use conditions. Travel model improvements can incorporate more of these factors are needed for TDM planning.


Conventional planning tends to consider a relatively limited set of demand variables, and so often fails to convey to decision-makers the full range of options that could be used to affect demand and solve problems such as congestion, accidents and pollution. For example, a planner might say that, since area population is growing 5% annually vehicle travel demand is projected to increase at the same rate so roadway capacity must be expanded to satisfy people’s need and avoid congestion problems. This assumes that demand is a fixed value (an inflexible number of vehicle-kilometers per capita). More detailed demand analysis can give decision-makers a wider range of options for solving programs. For example:


·         Conventional evaluation tends to focus on factors that are easier to quantify, such as travel speed and price, and gives less consideration to qualitative factors such as convenience, comfort and prestige. This tends to favor highway expansion and fuel subsidies over improvements in walking and cycling conditions, more convenient public transit user information, or more comfortable transit stations and vehicles.


·         Conventional planning tends to accept per capita vehicle ownership and travel demand as relatively inflexible values, implying that parking facilities and roadways should be expanded as needed to meet consumers’ demands and avoid congestion.


·         If demand is tested using cost-recovery Pricing (that is, the analysis investigates whether users would directly pay roadway expansion costs) demand often declines: motorists want additional roadway capacity only if it is financed indirectly, but not if they must pay for it directly. This indicates that roadway expansion project are economically inefficient and would reduce consumer welfare.


·         Travel demand analysis should consider interactive effects of all modes. In some situations, improvements to alternative modes (such as rideshare programs or better public transit service) can reduce the point of Congestion Equilibrium (that is, reduce the level of congestion at which travelers shift from driving to alternative modes).


·         If planners consider land use impacts on travel demand (for example, automobile-oriented development increases automobile travel demand, while transit-oriented development increases transit demand), Smart Growth strategies, such as more Transit-Oriented Development, can be considered as possible solutions to transport problems.



Improved demand analysis considers a wide range of factors that influence travel activity. This is important for Transportation Demand Management planning, which requires information on factors that influence transport activities, such as:










Wit and Humor

A vision without a plan is just a dream. A plan without a vision is just drudgery. But a vision with a plan can change the world.  —Anonymous



Best Practices

Planners, public officials and other stakeholders should become familiar with the concept of travel demand and the wide range of factors that can affect it. The Travel Model Improvement Program ( and similar resources can provide guidance on ways to effectively incorporate more factors when modeling demand.



Examples and Case Studies


Transport Analysis Guidance (

The UK Department For Transport provides detailed guidance on methods for modeling the effects of transportation system changes, including:

·         Methods of estimating changes in road traffic congestion as a result of a public transport programs.

·         Advice on the development of road traffic assignment models, how to achieve convergence of these models.

·         Development of public transport passenger assignment models.

·         Assignment methods.

·         Generalised cost analysis.

·         Convergence and validation of public transport passenger assignment models.

·         Design and conduct of travel demand surveys required for public transport model development.

·         Creation of matrices of public transport passenger trips.

·         Responses of public transport operators to changes in demand.



Policy Evaluation Models

Hensher (2002) developed an integrated urban passenger transport model system for evaluating the travel and environmental impacts of various policy instruments, such as road, parking and fuel pricing; incentives to purchase more efficient and alternative fuel vehicles; improvements to alternative modes; and smart growth land use policies. The model system has four integrated modules defining household location and automobile choices, commuter workplace and commuting travel choices, non-commuting travel activity, and employment location. The demand model system, estimated as a set of discrete and continuous choice models, is combined with a set of equilibrating criteria in each of the location, automobile and commuting markets to predict overall demand for passenger travel in various socio-economic segments, automobile classes and geographic locations. The system is applyed to Perth (Western Australia) to evaluate policy impacts on greenhouse gas emissions. The model system is embedded within a decision support system to make it an attractive of tool for practitioners.



Non-Motorized Travel Demand (

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



National Transport Model (

The UK National Transport Model (NTM) provides a systematic means of comparing the national consequences of alternative national transport policies or widely-applied local transport policies. It includes detailed data on motor vehicle ownership and travel demand.



Transit Ridership Models

A study by Thompson, et al. (2012) examined the factors underlying transit demand in the multi-destination, integrated bus and rail transit network for Atlanta, Georgia. Using data obtained from the 2000 Census, coupled with data obtained from local and regional organizations in the Atlanta metropolitan area, the researchers estimated several statistical models that explain the pattern of transit commute trips across the Atlanta metropolitan area. The models show that bus riders and rail riders are different, with bus riders exhibiting more transit-dependent characteristics and rail riders more choice rider characteristics. However, both types of riders value many of the same attributes of transit service quality (including shorter access and egress times and more direct trips) and their use of transit is influenced by many of the same variables (including population and employment). At the same time, the factors that influence transit demand vary depending on the type of travel destination the rider wishes to reach, including whether it is the central business district (CBD) or a more auto-oriented, suburban destination.


The results suggest that more direct transit connections to dispersed employment centers, and easier transfers to access such destinations, will lead to higher levels of transit use for both transit-dependent and choice riders. The results also show that the CBD remains an important transit destination for rail riders but not for their bus rider counterparts. Certain types of transit-oriented development (TOD) also serve as significant producers and attractors of rail transit trips.



TDM Models

Models are available which can predict the travel impacts of a specific Commute Trip Reduction program, taking into account the type of program and worksite. These include the TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) Model (, the CUTR_AVR Model (, the Business Benefits Calculator (BBC) ( and the Commuter Choice Decision Support Tool ( See Transportation Elasticities for information on the travel impacts that result from various price changes.



Université Laval TDM Survey (CDAT 2012)

A stated preference survey calculated the effects of changes in travel price (parking and transit fares) and time on commutes at the University of Laval, Quebec. It found that:

Combining these strategies increased the effects. For example, if public transit becomes free and the parking cost is increased 60%, automobile trips would decline 42%, which is more than the sum of the effects of each measure taken separately).



Modeling Travel Behavior and Policy Impacts (Robert, 2007)

Marcus Robert used discrete choice models to predict how people respond to various travel options and incentives, such as employers who encourage Telework and Ridesharing. The results were then incorporated into a traffic network equilibrium model, which quantified how changes in travel behavior affect road traffic, and traffic congestion, emissions, accidents and travel times.



Integrated Modeling For Sustainability And Welfare Evaluation

Johnston (2008) describes development of a statewide integrated transportation/land use urban growth model that can be used to evaluate major transportation scenarios in California, such as freeway widenings and high speed rail, and transport and land use policies intended to provide for more-affordable housing accessible to jobs, widespread habitat protection, and strong reductions in greenhouse gases. This model provides various performance measures, including travel activity, economic welfare and equity, rents paid, energy use, greenhouse gas emissions, vehicular air pollution, and habitat loss. The results are structured to reflect recent advances in the theories of well-being for persons and for nations.



Non-Motorized Transportation (

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



Oregon Transportation and Land Use Model Integration Program (

The Oregon Modeling Improvement Program (OMIP) is developing a Transportation and Land Use Model Integration Program (TLUMIP), an integrated transportation, land use and economic model for use in transportation planning and policy analyses at the regional and statewide levels. The first generation of the model, called Oregon1, has been successfully applied to several complex policy issues. Using information gained from these initial applications, Oregon2 is significantly refining and expanding elements of the program in a state-of-the-art modeling framework. This framework covers Oregon’s 36 counties and parts of adjoining states. It operates at various levels of geography, including a 30-meter grid of study area land use.



References And Resources For More Information


AARP (2012), Impact of Baby Boomers on U.S. Travel, 1969-2009, American Association of Retired Persons (; at


Bhuiyan Alam, Hilary Nixon and Qiong Zhang (2015), Investigating The Determining Factors For Transit Travel Demand By Bus Mode In Us Metropolitan Statistical Areas, Mineta Transportation Institute (; at


APTA (2007), A Profile of Public Transportation Passenger Demographics and Travel Characteristics Reported in On-Board Surveys, American Public Transportation Association (; at


APTA (2011), Potential Impact of Gasoline Price Increases on U.S. Public Transportation Ridership, 2011 -2012, American Public Transportation Association (; at


Gary Barnes and Kevin Krizek (2005a), Estimating Bicycling Demand, Transportation Research Board Annual Meeting (; at


Gary Barnes and Kevin Krizek (2005b), Tools for Predicting Usage and Benefits of Urban Bicycling, Humphrey Institute of Public Affairs, University of Minnesota (


Edward Beimborn, Rob Kennedy and William Schaefer (1996), Inside the Blackbox: Making Transportation Models Work for Livable Communities, Center for Urban Transportation Studies University of Wisconsin-Milwaukee (; at


Calthorpe Associates (2010), The Role of Land Use in Reducing VMT and GHG Emissions: A Critique of TRB Special Report 298, Calthorpe Associates (; at

CDAT (2012), “Reducing Automobile Dependency at Université Laval,” EnerInfo, Vol. 17, No. 1, Winter, pp. 1-2; at


Robert Cervero (2006), “Alternative Approaches to Modeling the Travel-Demand  Impacts of Smart Growth” Journal of the American Planning Association, Vol. 72, No. 3, Summer; at


Pasquale Colonna (2009), “Mobility and Transport For Our Tomorrow Roads,” EuropeanRoads Review, No. 14 (, RGRA, Spring, pp. 44-53.


CTS (2009), Understanding the Impacts of Transitways: Demographic and Behavioral Differences between Hiawatha Light-Rail and Other Transit Riders, Transitway Impacts Research Program (TIRP),  Center for Transportation Studies, University of Minnesota (; at


Joyce Dargay, Dermot Gately and Martin Sommer (2007), Vehicle Ownership and Income Growth, Worldwide: 1960-2030, New York University; at


The Demand Institute ( is a research institute which uses innovative approaches to defining energy demand and more resource efficient ways to satisfy those needs.


DFT (2006), Transport Analysis Guidance, Integrated Transport Economics and Appraisal, Department for Transport ( 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.


DfT (2010), National Transport Model, Integrated Transport Economics and Appraisal, Department for Transport (; at


Distillate ( (Design and Implementation Support Tools for Integrated Local LAnd use, Transport and the Environment) is a research project to help overcoming the barriers to the effective development and delivery of sustainable urban transport and land use strategies.


DKS Associates (2003), Modeling TDM Effectiveness, Washington Department of Transportation (


DKS Associates (2007), Assessment of Local Models and Tools For Analyzing Smarth Growth Strategies, California Department of Transportation (; at


Xiaojing Dong, Moshe Ben-Akiva, John Bowman and Joan Walker (2006), “Moving From Trip-Based to Activity-Based Measures of Accessibility,” Transportation Research A, Volume 40, Issue 2 (, Feb. 2006, pp. 163-180.


Liisa Ecola, et al. (2014), The Future of Driving in Developing Countries, RAND Institute for Mobility Research (; at


Raquel Espinoa, Juan de Dios Ortúzarb and Concepción Román (2007), “Understanding Suburban Travel Demand: Flexible Modelling With Revealed And Stated Choice Data,” Transportation Research Part A: Policy and Practice (, Volume 41, Issue 10, December 2007, pp. 899-912


Ann Forsyth, Kevin J. Krizek and Asha Weinstein Agrawal (2010), Measuring Walking and Cycling Using the PABS (Pedestrian and Bicycling Survey) Approach: A Low-Cost Survey Method for Local Communities, Mineta Transportation Institute, San Jose State University (; at


Michael Iacono, Kevin Krizek and Ahmed El-Geneidy (2008), Access to Destinations: How Close is Close Enough? Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes, Report 2008-11, University of Minnesota (; at


ICE (2006), Assessment of Integrated Transportation/Land Use Models, Information Center for the Environment, UC Davis, for California Department of Transportation; at


Robert Johnston (2006), Review of U.S. and European Regional Modeling Studies of Policies Intended to Reduce Motorized Travel, Fuel Use, and Emissions, Environmental Science & Policy, University of California, Davis; at


Karla H. Karash, et al. (2008), Understanding How Individuals Make Travel and Location Decisions: Implications for Public Transportation, TCRP Report 123, Transit Cooperative Research Board (; at


Rod Katz (2001), Forecasting Demand for Bicycle Facilities, Austroads, ISBN 0 85588 6901 3 (


Kevin J. Krizek, Gary Barnes, Ryan Wilson, Bob Johns and Laurie McGinnis (2007), Nonmotorized Transportation Pilot Program Evaluation Study, Active Communities Transportation Research Group and the Center for Transportation Studies, University of Minnesota (; at


J. Richard Kuzmyak, et al. (2014), Estimating Bicycling and Walking for Planning and Project Development: A Guidebook, NCHRP Report 770, Transportation Research Board (; at


Todd Litman (2001), “You Can Get There From Here: Evaluating Transportation Choice,” Transportation Research Record 1756, Transportation Research Board (, pp. 32-41; at


Todd Litman (2001), “Generated Traffic; Implications for Transport Planning,” ITE Journal, Vol. 71, No. 4, Institute of Transportation Engineers (, April, 2001, pp. 38-47; at


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


Todd Litman (2005), Transportation Cost and Benefit Analysis Guidebook, Victoria Transport Policy Institute (


Todd Litman (2006a), Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, Victoria Transport Policy Institute (; at .


Todd Litman (2006b), The Future Isn’t What It Used To Be: Changing Trends And Their Implications For Transport Planning, Victoria Transport Policy Institute (; at; originally published as  “Changing Travel Demand: Implications for Transport Planning,” ITE Journal, Vol. 76, No. 9, (, September, pp. 27-33.


Todd Litman (2007), Build For Comfort, Not Just Speed: Valuing Service Quality Improvements In Transport Planning, VTPI (; at


Todd Litman (2007), Evaluating Accessibility for Transportation Planning, Victoria Transport Policy Institute (; at .


Todd Litman (2008), Land Use Impacts On Transport: How Land Use Factors Affect Travel Behavior, VTPI (; at


Todd Litman (2009), Where We Want To Be: Home Location Preferences And Their Implications For Smart Growth, Victoria Transport Policy Institute (; at  


Todd Litman (2009), “Mobility as a Positional Good: Implications for Transport Policy and Planning,” Car Troubles: Critical Studies of Automobility and Auto-Mobility (Jim Conley and Arlene Tigar McLaren eds), Ashgate (; Introduction at; Litman’s paper at


Todd Litman (2010), Short and Sweet: Analysis of Shorter Trips Using National Personal Travel Survey Data, VTPI (; at


Todd Litman (2011), “Adjusting Data Collection Methods: Making the Case for Policy Changes to Build Healthy Communities,” From Inspiration to Action: Implementing Projects to Support Active Living, American Association for Retired Persons ( and Walkable and Livable Communities Institute (, pp. 104-107; at


Todd Litman (2012), Changing Vehicle Travel Price Sensitivities: The Rebounding Rebound Effect, Victoria Transport Policy Institute (; at;


Todd Litman (2013), “Changing North American Vehicle-Travel Price Sensitivities: Implications For Transport And Energy Policy,” Transport Policy, Vol. 28, July, pp. 2-10; summary at


Todd Litman (2013), “The New Transportation Planning Paradigm,” ITE Journal (, Vol. 83, June, pp. 20-28; at


Todd Litman (2012), “Current Mobility Trends – Implications for Sustainability,” chapter in Keep Moving, Towards Sustainable Mobility, Eleven International (, pp. 23-44; at


Todd Litman (2012), Smart Congestion Relief: Comprehensive Analysis Of Traffic Congestion Costs and Congestion Reduction Benefits, Victoria Transport Policy Institute (; at; paper P12-5310, presented at the Transportation Research Board Annual Meeting (


Ian M. Lockwood (2004), Transportation Prescription For Healthy Cities, Glatting Jackson Transportation Urban Design Studio, for presentation and Common Ground


William Loudon and Terry Parker (2008), Modeling the Travel Impacts of Smart-Growth Strategies, Transportation Research Board 87th Annual Meeting (


MOTOS (Transport Modelling: Towards Operational Standards) ( is a European program to define best practices for national and regional transport modelling.


NAR (2013), National Community Preference Survey, National Association of Realtors (; at


NBRTI (2009), Quantifying the Importance of Image and Perception to Bus Rapid Transit, National Bus Rapid Transit Institute ( for the Federal Transit Administration; at


Steven Polzin and Xuehao Chu (2007), Exploring Long-Range U.S. Travel Demand: A Model for Forecasting State-Level Person Miles and Vehicle Miles of Travel for 2035 and 2055, Office of Policy, Federal Highway Administration.


Steven E. Polzin, Xuehao Chu and Nancy McGuckin (2011), Exploring Changing Travel Trends, presented at Using National Household Travel Survey Data for Transportation Decision Making: A Workshop, Transportation Research Board (; at


Richard H. Pratt (2005), Traveler Response to Transportation System Changes Handbook, TCRP, TRB (; at


PROSPECTS (2003), Transport Strategy: A Decisionmakers Guidebook, Konsult, Institute for Transport Studies, University of Leeds (; at; originally published as, Developing Sustainable Urban Land Use and Transport Strategies: A Methodological Guidebook; at


Robert Puentes (2008), The Road…Less Traveled: An Analysis of Vehicle Miles Traveled Trends in the U.S., Brooking Institution (; at


Michael Sivak (2013), Has Motorization in the U.S. Peaked?, University of Michigan, Transportation Research Institute (; at This is the first in a series of reports which examine various factors that are contributing to peaking motor vehicle travel.


Peter R. Stopher and Stephen P. Greaves (2007), “Household Travel Surveys: Where Are We Going?,” Transportation Research A, Vol. 41, Issue 5 (, June 2007, pp. 367-381.


SUMMA (2003), Fast Simple Model, SUMMA (Sustainable Mobility, Policy Measures and Assessment) ( This is a model for operationalizing the concept of sustainable transportation by predicting the impacts of various policies and programs.


Gregory Thompson, et al. (2012), Understanding Transit Ridership Demand For A Multi-Destination, Multimodal Transit Network In An American Metropolitan Area: Lessons For Increasing Choice Ridership While Maintaining Transit Dependent Ridership, Mineta Transportation Institute (; at


Toolbox for Regional Policy Analysis Website ( by the US Federal Highway Administration, describes analytical methods for evaluating regional economic, social and environmental impacts of various transportation and land use policies.


Traffic Analysis Tools Website (, Office of Operations, Federal Highway Administration.


Travel Model Improvement Program ( is a U.S. Department of Transportation program to support research and information sharing for improving travel models.


TRICS ( is a consortium of UK regional councils to collect and distribute trip generation data based on thousands of transport surveys. This provides convenient and accurate information on the trip generation of various types of development, and the effectiveness of various mobility management strategies.


TRB (2007), Metropolitan Travel Forecasting: Current Practice and Future Direction, Special Report 288, Transportation Research Board (; at


TRL (2004), The Demand for Public Transit: A Practical Guide, Transportation Research Laboratory, Report TRL 593 (; at


USEPA (2002), Smart Growth Index (SGI) Model, U.S. Environmental Protection Agency ( For technical information see Criterion, Smart Growth Index Indicator Dictionary, U.S. Environmental Protection Agency (


USEPA (2005), Commuter Model, U.S. Environmental Protection Agency (


USF (2006), TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) Model, developed by the University of South Florida ( evaluates the travel impacts, benefits and costs of various commute trip reduction programs and other mobility management strategies; at


Clark Williams-Derry (2011), Dude, Where Are My Cars? Sightline Institute (; at

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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