3 Transportation Demand Management

Chapter Overview

Travel demand management (TDM) involves a variety of strategies to influence travel behavior and encourage the use of sustainable alternatives. This chapter defines and classifies TDM and discusses the relationship between TDM, mode choice, and changes in travel mode. TDM strategies included in this chapter may be classified into three general categories ─ push vs. pull, generalized vs. personalized, and governmental vs. employer based.

Chapter Topics

  1. What is TDM?
  2. Benefits of TDM
  3. Classifying TDM Measures
  4. TDM Performance Measures

Learning Objectives

At the completion of this chapter, readers will be able to:

  • Define travel demand management (TDM) and related terms
  • Classify TDM strategies from different perspectives, relating TDM to multimodal options
  • Differentiate governmental policies from employer based TDM tools
  • Identify TDM performance measures
  • Explain how TDM tools can be applied to promote multimodal options in a community or region in support of a multimodal plan


The need for a multimodal environment and efficient transportation system is influenced by numerous things. Population growth, urban development, and other socioeconomic factors can transform the travel pattern of a city or region (Bigazzi & Figliozzi, 2012; Davison & Knowles, 2006). The resulting increase in traffic can eventually lead to congestion and the deterioration of roadways. With these factors comes a corresponding deterioration of the urban environment and social atmosphere that many crave in an urban experience (Bigazzi & Figliozzi, 2012). Congestion impedes the desire for travel and reduces the opportunity for social and economic activities.

Congestion can also create a snowball effect on a local or regional transportation network. With more congestion comes more crashes, which leads to more congestion and the cycle continues (Wang et al., 2009). The economy cannot be productive if the transportation system is inefficient, unsafe or nonexistent (Li et al., 2021). Coordination among local, state and federal agencies and often with the private sector is necessary to combat these factors to reduce congestion and create an efficient transportation system (B. D. Taylor, 2004; Teodorović & Dell’Orco, 2008). Travel demand management or TDM involves such coordination and is widely acknowledged as a cost-effective way to manage traffic congestion and promote multimodal options.

What is TDM?

Travel Demand Management (TDM) refers to the use of policies, strategies, and programs to increase overall system efficiency. This is primarily accomplished by encouraging a modal shift from driving alone to more sustainable alternatives, such as transit, ridesharing, biking and walking, or by reducing travel during peak periods, such as through variable work hours and telecommuting. TDM strategies have involved the use of incentives and interventions to change the way people travel and discourage the use of solo rides to and from destinations. These incentives and interventions are often informational, social, or financial strategies (Bianco, 2000; Castellanos, 2016; Ghimire & Lancelin, 2019; Li et al., 2021).

As an effective way to encourage mode shift and promote the use of multimodal options, TDM is essential to multimodal planning. TDM strategies aim to encourage multimodality ─ defined in the literature as the use of more than one mode of transportation in a given period (An et al., 2021; Heinen & Mattioli, 2019). TDM strategies also focus on sustainability, which often goes hand and hand with multimodality. TDM is primarily achieved by local agencies and employers and involves distributing incentives, advertising, adding infrastructure, and restricting the use of less sustainable modes of transportation (Shin, 2020; Zaman & Habib, 2011).

Benefits of TDM

The promotion of multimodality, either through TDM strategies or other strategies, provides a variety of socio-economic, environmental, and health benefits (Klinger, 2017). These benefits accrue to all parties, including individuals, the public, employers, and the government. Some examples include more access to jobs on extensive public transportation systems for citizens without cars, reduced carbon emissions with fewer cars on the road, and increased economic output from reduced congestion and more easily accessible stores.

Employers who support sustainable transportation might either obtain a tax abatement or avoid paying an additional tax (Ko & Kim, 2017; Shaheen et al., 2018). The incentives for employees who are being encouraged to take other modes of transportation include subsidies from the company, better health from walking or biking, faster commute time, and lower stress commutes (Shin, 2020).

Classifying TDM Measures

TDM emphasizes the importance of saving energy, controlling air quality, improving systems to meet the demands of more people efficiently, reducing car dependency, and increasing active modes of transportation. These broad TDM goals and related strategies may be categorized into three distinct groups: pull vs. push, generalized vs. personalized, and government vs. employer-based (Hasnine et al., 2017; Keizer et al., 2019; Ko & Kim, 2017; Li et al., 2021). The following sections describe how each classification helps characterize TDM strategies.

Pull vs. Push

Pull strategies involve offering incentives, information, and alternative choices to encourage changes in travel behavior, curb congestion, and promote multimodal transportation (see Table 3.1). These strategies include providing modal alternatives, informing travelers with real-time travel conditions, subsidizing transit, rewarding travelers for not driving alone and/or using other modes, and improving/building the infrastructure for biking and walking. Alternatively, push side strategies are more extreme and include government policies or actions that make it more difficult or expensive to drive alone. Although seemingly harsh, these strategies can effectively push people into taking alternative transportation modes. Examples include parking pricing, tolling, restricted roads and license plate auctions (Bianco, 2000; Shin, 2020). Implementing both push and pull strategies can help promote travel behavior changes, effectively reducing congestion and managing traffic.

Table 3. 1. Examples of Pull vs. Push TDM Measures

Pull Measures

(Attract travelers to alternative modes and schedules)

Push Measures

(Discourage single occupant vehicle or SOV travel)

Improve alternative modes Introduce pricing on roads and parking
Integrate different travel modes Impose restrictions on SOV use
Provide alternative work schedules Restrict the supply of free parking
Offer incentives and subsidies Employ land use/zoning tools, such as transit-oriented development
Use new technologies

Generalized vs. Personalized

Generalized vs. personalized strategies are the most straightforward of the three TDM strategy classifications (see Table 3. 2 and Table 3. 3). Generalized strategies are policies that pertain to all people. These strategies include common policies such as tolling and parking prices, as well as other generalized policies such as carpooling lanes and car-free zones. So far these strategies have not been overtly effective in reducing solo driving (Gössling & Cohen, 2014; Sammer & Saleh, 2009). An obvious shortfall of generalized strategies is that a one-size-fits-all incentive is not likely to address the transportation needs of many individuals.

In contrast, personalized strategies are designed to encourage voluntary travel behavior change for certain individuals or groups (Meloni et al., 2012). Voluntary behavior change is when individuals decide to make changes without any external influences (M. A. Taylor, 2007). Personalized strategies encourage individuals to change to more sustainable alternatives by fostering individual responsibility and promoting a healthy lifestyle (Di Dio et al., 2015); ideally establishing desired habits. Personalized incentives for behavior change are also supported by the increased use of personal devices such as smartphones, which are equipped with sensors (e.g., GPS, accelerometers, gyroscopes) and continuous access to the internet (Jariyasunant et al., 2015; Ma et al., 2015; Teulada & Meloni, 2016; Tulusan et al., 2012).

Table 3. 2. Examples of Generalized TDM Tools
Alternative management Incentive management Land use management
HOV lane Parking pricing Smart growth
Carshare program Tolling Transit-oriented development
Transit improvements Fuel tax increase Parking management
Highway expansion License auction
Complete streets


Table 3. 3. Examples of Personalized TDM Tools
Alternative management Incentive management Land use management Schedule management
Ride match Distance-based pricing Proximate commute Flex time
Guaranteed ride home Parking cash-out Telework
Emergency ride Transit subsidy Compressed work week
Real-time traffic information Travel feedback

Governmental vs. Employer-based

The final subsection of TDM strategies is governmental vs. employer-based strategies. As entities responsible for planning and funding transportation systems, government agencies have developed many strategies to encourage individuals to use transportation alternatives. Governmental strategies tend to be more generalized, rather than catering to individual needs. Businesses, corporations, and other major employers have an incentive to provide TDM strategies as heavy traffic during peak hour commuting times can reduce employee productivity, increase tardiness, and impact morale.

Employer-based TDM strategies, also known as commute trip reduction or workplace travel plan, generally encompass three approaches to reducing peak hour congestion (Ko & Kim, 2017; Shin, 2020):

  1. Discourage solo driving to increase sustainable alternatives,
  2. Increase and promote carpooling/vanpooling among coworkers, and
  3. Allow flexible schedules such as shorter work weeks, telecommuting, and staggered hours.

While not common for smaller companies, some companies employ a transportation coordinator to offer employees easy access to alternative transportation and day-to-day transportation planning.

In the 1980s, a nationwide Commute Trip Reduction (CTR) program was initiated by the Environmental Protection Agency (EPA) to reduce single-occupant commuting trips during peak hours (Oren, 1998a). Directed by the Employee Commute Options Guidance issued by the EPA, the states selected by the program were obligated to implement statewide and regional plans to reduce traffic congestion and vehicular emissions (Dill, 1998; Oren, 1998a). Major employers (more than 100 employees) were also called upon to use these new strategies to entice employees to seek alternative transportation methods.

Many federal CTR programs had limited success and were terminated due to a lack of hierarchy, insufficient support among state and regional governments, declining public opinion of the program, and issues with the deployment of the strategies (Burns, 1992; Dill, 1998; Giuliano et al., 1993; Oren, 1998a, 1998b, 1998c). Congress and the EPA ultimately abolished the CTR mandate in the mid-1990s (Oren, 1998a, 1998b, 1998c), and most CTR statewide programs quickly vanished, except in Washington State.

Despite the termination of federal programs, many cities kept CTR programs in place even without federal mandates. With the decline of the federally-funded statewide CTR program, employer-based TDM is becoming more localized. Cities like Washington DC., Atlanta, San Francisco, Los Angeles, Houston, and Denver kept their regional CTR programs (Ghimire and Lancelin, 2019; Herzog et al., 2006; Zhou et al., 2012; Zuehlke and Guensler, 2007). Many international cities have also implemented employer-based TDM strategies, such as Perth-Australia, Xian-China, Seoul-South Korea, Toronto-Canada, and Antwerp/Brussels-Belgium (Hasnine et al., 2017; Ko and Kim, 2017; Vanoutrive, 2019; Wake, 2007; Zhu and Fan, 2018).

TDM Performance Measures

Indicators for assessing the effectiveness of TDM programs and strategies typically include average vehicle ridership (AVR) (Giuliano et al., 1993; Stewart, 1994; Winters et al., 2005), vehicle trip rates (VTR, the inverse of AVR), and vehicle miles traveled (VMT) (Lagerberg, 1997; Lopez-Aqueres, 1993). Other performance metrics for evaluating the effectiveness of TDM include modal shifts, emissions, level of service (LOS), and monetary costs (Lopez-Aqueres, 1993). Additional information on performance measures and methods for evaluating the performance of multimodal transportation systems and strategies can be found in Chapter 8.

Case Examples

Congestion Pricing: London, England

The London metropolitan area is home to about 14 million residents, not including daily visitors, increasing the need for extensive automobile infrastructure in a historic European capital not designed to accommodate large numbers of vehicles. To address this problem, London has implemented numerous policies and transportation alternatives, with several successes. An extensive rail network, emphasis on walkability, and safety measures for pedestrians and bicycles have all helped reduce congestion.

Congestion pricing has perhaps been the most effective strategy applied in London, serving as a case study for implementing congestion pricing systems in larger cities around the world. Congestion pricing is a push strategy that charges certain vehicles a flat fee to drive their vehicle on public roads. Certain vehicles are exempt from this “tax” including alternative fuel vehicles, vehicles used by citizens with disabilities, taxis, and motorcycles. The congestion pricing program reduced the number of car trips into the central city, congestion waiting times for buses, and overall congestion for all vehicles (Litman, 2005).

Tolling: Florida Turnpike

Florida’s turnpike system was designed to alleviate pressure from the interstate highway system and provide Floridians with better access to major destinations. Decades after the completion of the turnpike, the system is still successful, but congestion mitigation methods were still needed in this rapidly growing state. The State of Florida turned to tolling for additional help in travel demand management and congestion reduction.

Tolling is the process of charging a motorized vehicle a fee to use the road, which may be categorized as a push as well as a generalized TDM tool. The science behind tolling is that if people are charged a small fee to travel, they will opt for more economical options, such as carpooling, less-traveled roads, or public transportation. By using a tolling program, Florida is able to push citizens into more sustainable and economical modes of transportation while also raising a substantial amount of money to provide other transportation services, such as Bus Rapid Transit (BRT) and commuter rail.

Florida’s tolling system works in multiple ways and employs multiple strategies to reduce congestion. The introduction of tolled express lanes, or High Occupancy Toll (HOT) lanes, on I-95 in Miami, has helped reduce congestion on the main I-95 general-use lanes and reduced travel time for both HOT and general-use lanes. An alternate method to HOT lanes was implemented in Lee County, Florida where discount tolling was used to decrease commuters on main roads and increase commuters on less traveled expressways and roads. This strategy led to 71% of commuters shifting their commute routes (FHWA, 2020).

Performance-based Parking: San Francisco

In large, congested cities it can be difficult to find parking. Drivers circulate looking for a place to park, wasting fuel and contributing to congestion. Cities like San Francisco, with the help of the federal government, have designed new TDM policies to reduce the number of people looking for parking. SFPark was one of the first performance-based parking pricing systems in the country. The program uses smart parking meters that assess the performance of a parking spot and base all pricing on that performance or demand ─ a basic supply and demand strategy that pushes people to circulate less in search of a parking space.

For example, if a row of parking spaces in any given neighborhood is full in the morning but vacant in the afternoon, the pricing for those spaces will be higher in the morning than in the afternoon. Customers can use the app or go online to see what spots are open in real time, how much time is left on occupied spots, and pay through the phone or with a credit card. The results for San Francisco have been beneficial, leading to a decrease in circulation and an increase in vacant parking spots. Allowing people to predict where they will find a parking spot and just how much they will pay leads to a decrease in needless circulation and congestion on city streets (Chatman & Manville, 2014).

Commute Trip Reduction: Washington State

Commute trip reduction, or CTR, is a package of pull policies to reduce driving during peak travel hours. CTR incentivizes non-single-occupant vehicle travel and the use of alternative modes of transportation by commuters. Persons that commute during peak hours are targeted with incentives, such as free or discounted fees for rail commuting, bus commuting, or biking and walking.

The Washington State Department of Transportation (WSDOT) designed a CTR program for a nine-county region that targeted peak-hour commuters. Although designed by WSDOT, the program was implemented by the counties. It includes incentives and other strategies such as providing information on alternate modes, offering alternative work schedules, use of parking management, offering shared mobility options, construction of new bus stops, and construction of bike lanes, as well as bike safety measures. The Washington CTR program has resulted in the reduction of 34,000 vehicles on the road per week, saving commuters $25 million and removing 75,000 metric tons of greenhouse gas emissions (WSDOT, 2018).

Subsidized Transit and TOD: Hong Kong, NYC;

Transit-oriented development (TOD) includes the development of housing units at or near transit facilities to boost ridership and provide residents with an easy commute option. Subsidized transit includes the development of services at or near transit stations, refurbishment of stations, money to keep and expand transit services, and beautification/cleanliness programs for transit services. The thought process behind this strategy is simple – if people have access to services, such as small grocery stores, and are presented with a clean and safe station, they will be more inclined to use that specific transportation method. Large cities like Hong Kong and New York City, have employed TOD and other subsidies for transit services to boost ridership and reduce cars on already congested roads. In both cities, these strategies resulted in an increase in ridership (Loo et al., 2010). Research also shows that station services and cleanliness lead to the largest increase in ridership (Loo et al., 2010).

Micromobility Programs: Portland Oregon

As a pull and generalized approach, micromobility refers to transportation modes that are small, lightweight, and fits readily in crowded urban spaces, such as bikes and scooters. Micromobility modes have become popular in congested core areas and downtown settings where it is harder to drive and find parking. Smartphone apps, kiosks, and sometimes free rides have allowed persons that live, work, and play in downtown areas to reduce their reliance on cars and shift to smaller, more sustainable modes.

Portland, Oregon has implemented public and private micromobility services such as shared e-scooters in the downtown core. Electric scooters can be used in the downtown core and simply left at the destination. This minimizes the need to drive in traffic and locate parking, helping to reduce carbon emissions. Identical systems have been introduced in Tampa, Los Angeles, and Dallas (after a brief ban on scooter share programs for the latter). Portland also has an extensive bike share program called “BikeTown”. According to the Portland Bureau of Transportation (PBOT), these bike share and scooter share programs can help reduce carbon emissions by 50% of 1990 levels (Kuehn, 2019). PBOT expects its programs to increase micromobility usage from 7% in 2019 to 25% of all trips in Portland by 2035 (Kuehn, 2019).

Social Carpooling: Metropia

Carpooling is a pull strategy that encourages people to travel together to a common workplace. This strategy has been used for decades to reduce the number of vehicles, particularly during peak commuting hours. In the 21st century, technology and changes in society have allowed planners to better target carpooling programs. Mobility planning apps can now estimate the number of riders a route is shared with, match riders with preferred carpooling partners depending on their comfort level, and allow commuters to plan trips using multiple transportation modes. They can also be used for researching travel data to optimize the planning of city transportation networks.

Most commuters are no longer comfortable sharing a car with strangers without verified information and reassurances. Metropia is a mobility planning app that aims, among other things, to reduce risk and connect commuters with their desired carpool partner based on the comfort level of both commuters. Metropia’s carpooling and other services are targeted pull strategies that cater to the needs and realities of modern society. These services can also be mixed with conventional carpooling strategies, such as designated carpooling lanes, as well as financial incentives to entice drivers to commute with a partner. Metropia partnered with the City of Austin to allow users to get real-time information on traffic conditions during special events and to also match commuters with rideshare partners.

Research from the University of Arizona indicates that the promotion and introduction of a transportation “Supernetwork” such as Metropia could increase carpooling commuters to campus by 13.75% (Arian & Chiu, 2017). Similarly, research shows that 22% of all commuters can switch commutes to more sustainable modes of transportation, like carpooling. This research indicates that if pull strategies are properly executed, commuters will decide to take other transportation methods, including carpooling to a large degree.


Key Takeaways

Travel demand management is an integral part of multimodal transportation planning. It is important that planners understand the strategies they can use to influence the transportation modes citizens use. These policies and strategies, at their core, aim to increase the efficiency of transportation systems and influence citizen decisions when it comes to transportation. Travel demand management includes strategies in three distinct groups, pull vs. push, generalized vs. personalized, and governmental vs. employer-based. These distinct groups all influence travel differently and take different approaches to target certain citizens. Some key takeaways from this chapter include:

  • Travel demand management is the use of policies, strategies, and programs to increase overall system efficiency and reduce congestion. This is primarily accomplished by encouraging a modal shift from driving alone to more sustainable alternatives.
  • Travel demand management strategies are categorized into three distinct groups: pull vs. push, generalized vs. personalized, and government vs. employer-based.
    • Pull vs. push policies: Pull strategies involve offering incentives, information, and alternative choices to encourage changes in travel behavior, while push strategies include government policies or actions that make it more difficult or expensive to drive alone.
    • Generalized vs. personalized: Generalized strategies are policies that pertain to all people, while personalized strategies are designed to encourage voluntary travel behavior change for certain individuals or groups of people.
    • Government vs. employer-based: Government-based strategies are implemented by the government and targeted toward the general population, while employer-based strategies are more incentive-based and targeted toward employees.
  • Examples of successful TDM strategies examined in this chapter include tolling, performance-based parking, commute trip reduction programs, subsidized transit, micro-mobility options, and social carpooling.


Self Test



Congestion: The state of roadways when there is stopped or stop-and-go traffic. Congestion is characterized by high volumes of vehicles, vehicular queuing, slower speeds, and longer travel times.



An, Z., Heinen, E., & Watling, D. (2021). When you are born matters: An age-period-cohort analysis of multimodality. Travel Behaviour and Society, 22, 129–145.

Arian, A., & Chiu, Y.-C. (2017). Evaluating carpooling opportunities among multimodal options using a supernetwork approach (No. 17–06811). Article 17–06811. Transportation Research Board 96th Annual Meeting. https://trid.trb.org/view/1439729

Bianco, M. J. (2000). Effective transportation demand management: Combining parking pricing, transit incentives, and transportation management in a commercial district of Portland, Oregon. Transportation Research Record, 1711(1), 46–54.

Bigazzi, A. Y., & Figliozzi, M. A. (2012). Congestion and emissions mitigation: A comparison of capacity, demand, and vehicle based strategies. Transportation Research Part D: Transport Environment, 17(7), 538–547.

Burns, E. (1992). Arizona’s metropolitan travel reduction programs [Working Paper, No. 81]. University of California Transportation Center. https://escholarship.org/content/qt6vh6j3pz/qt6vh6j3pz_noSplash_59c5aeb319b94492bae9a9a6bb975916.pdf?t=mc2sx7

Castellanos, S. (2016). Delivering modal-shift incentives by using gamification and smartphones: A field study example in Bogota, Colombia. Case Studies on Transport Policy, 4(4), 269–278.

Chatman, D. G., & Manville, M. (2014). Theory versus implementation in congestion-priced parking: An evaluation of SFpark, 2011–2012. Research in Transportation Economics, 44, 52–60. https://doi.org/10.1016/j.retrec.2014.04.005

Davison, L. J., & Knowles, R. D. (2006). Bus quality partnerships, modal shift and traffic decongestion. Journal of Transport Geography, 14(3), 177–194.

Di Dio, S., Casto, B. L., Micari, F., Rizzo, G., & Vinci, I. (2015). Mobility, data, and behavior: The trafficO2 case study. In A. Vesco & F. Ferrero (Eds.), Handbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities (pp. 382–406). IGI Global. https://doi.org/10.4018/978-1-4666-8282-5.ch018

Dill, J. (1998). Mandatory employer-based trip reduction: What happened? Transportation Research Record, 1618(1), 103–110. https://doi.org/10.3141/1618-12

Ghimire, R., & Lancelin, C. (2019). The relationship between financial incentives provided by employers and commuters’ decision to use transit: Results from the Atlanta Regional Household Travel Survey. Transport Policy, 74, 103–113.

Giuliano, G., Hwang, K., & Wachs, M. (1993). Employee trip reduction in Southern California: First year results. Transportation Research Part A: Policy & Practice, 27(2), 125–137.

Gössling, S., & Cohen, S. (2014). Why sustainable transport policies will fail: EU climate policy in the light of transport taboos. Journal of Transport Geography, 39(C), 197–2017. https://doi.org/10.1016/j.jtrangeo.2014.07.010

Hasnine, M. S., Weiss, A., & Nurul Habib, K. (2017). Stated preference survey pivoted on revealed preference survey for evaluating employer-based travel demand management strategies. Transportation Research Record, 2651(1), 108–117.

Heinen, E., & Mattioli, G. (2019). Does a high level of multimodality mean less car use? An exploration of multimodality trends in England. Transportation, 46(4), 1093–1126.

Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sengupta, R., & Walker, J. L. (2015). Quantified traveler: Travel feedback meets the cloud to change behavior. Journal of Intelligent Transportation Systems, 19(2), 109–124. https://doi.org/10.1080/15472450.2013.856714

Keizer, M., Sargisson, R. J., van Zomeren, M., & Steg, L. (2019). When personal norms predict the acceptability of push and pull car-reduction policies: Testing the ABC model and low-cost hypothesis. Transportation Research Part F: Traffic Psychology & Behaviour, 64, 413–423.

Klinger, T. (2017). Moving from monomodality to multimodality? Changes in mode choice of new residents. Transportation Research Part A: Policy and Practice, 104, 221–237. https://doi.org/10.1016/j.tra.2017.01.008

Ko, J., & Kim, D. (2017). Employer-based travel demand management program: Employer’s choice and effectiveness. Transport Policy, 59, 1–9.

Kuehn, B. (2019). The introduction of micromobility and the future of Portland transportation. https://bucky.page/micromobility-future-portland-transportation/

Lagerberg, B. (1997). Washington State’s commute trip reduction program: Phase 1: Assessment and implications for program design. Transportation Research Record, 1598(1), 36–42.

Li, T., Chen, P., & Tian, Y. (2021). Personalized incentive-based peak avoidance and drivers’ travel time-savings. Transport Policy, 100, 68–80.

Litman, T. (2005). London congestion pricing – Implications for other cities. CESifo DICE Report, 3, 17–21.

Loo, B. P. Y., Chen, C., & Chan, E. T. H. (2010). Rail-based transit-oriented development: Lessons from New York City and Hong Kong. Landscape and Urban Planning, 97(3), 202–212. https://doi.org/10.1016/j.landurbplan.2010.06.002

Lopez-Aqueres, W. (1993). Conceptual framework to study the effectiveness of employer trip-reduction programs. Transportation Research Record, 1404, 55.

Ma, L., Mulley, C., & Liu, W. (2015). Social marketing and the built environment: What matters for travel behaviour change? https://doi.org/10.1007/S11116-016-9698-2

Meloni, I., Sanjust, B., & Spissu, E. (2012). Soft transport policy measures: Implementing personalised travel planning. European Transport Conference 2012, Location: Glasgow, Scotland. https://aetransport.org/past-etc-papers/conference-papers-2012

Oren, C. N. (1998a). Detail and implementation: The example of employee trip reduction. Virginia Environmental Law Journal, 17(2), 123–186.

Oren, C. N. (1998b). Getting commuters out of their cars: What went wrong? Stanford Environmental Law Journal, 17(141), 106.

Oren, C. N. (1998c). How a mandate came from hell: The making of the federal employee trip reduction program. Environmental Law, 28(2), 267–373.

Sammer, G., & Saleh, W. (2009). Travel demand management and road user pricing: Success, failure and feasibility (1st ed.). Routledge. https://www.routledge.com/Travel-Demand-Management-and-Road-User-Pricing-Success-Failure-and-Feasibility/Sammer-Saleh/p/book/9780754673033

Shaheen, S., Cohen, A., & Bayen, A. (2018). The benefits of carpooling. UC Berkeley Working Paper.

Shin, E. J. (2020). Commuter benefits programs: Impacts on mode choice, VMT, and spillover effects. Transport Policy, 94, 11–22.

Stewart, J. (1994). Reducing drive-alone rates at small employer sites: Costs and benefits of local trip reduction ordinances-Pasadena towers case study. Transportation Research Record, 159–159.

Taylor, B. D. (2004). The politics of congestion mitigation. Transport Policy, 11(3), 299–302.

Taylor, M. A. (2007). Voluntary travel behavior change programs in Australia: The carrot rather than the stick in travel demand management. International Journal of Sustainable Transportation, 1(3), 173–192.

Teodorović, D., & Dell’Orco, M. (2008). Mitigating traffic congestion: Solving the ride-matching problem by bee colony optimization. Transportation Planning & Technology, 31(2), 135–152.

Teulada, B. S. di, & Meloni, I. (2016). Individual persuasive eco‐travel technology, a mobile persuasive application for implementing voluntary travel behaviour change programmes. IET Intelligent Transport Systems, 10(4). https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-its.2015.0198

Tulusan, J., Staake, T., & Fleisch, E. (2012). Providing eco-driving feedback to corporate car drivers: What impact does a smartphone application have on their fuel efficiency? Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 212–215. https://doi.org/10.1145/2370216.2370250

Wang, C., Quddus, M. A., & Ison, S. G. (2009). Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England. Accident Analysis & Prevention, 41(4), 798–808.

Winters, P. L., Perez, R. A., Joshi, A. D., & Perone, J. (2005). Work site trip reduction model and manual. Transportation Research Record, 1924(1), 197–206.

Zaman, H., & Habib, K. M. N. (2011). Commuting mode choice in the context of travel demand management (TDM) policies: An empirical investigation in Edmonton, Alberta. Canadian Journal of Civil Engineering, 38(4), 433–443.




Icon for the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Multimodal Transportation Planning Copyright © 2024 by Peng Chen; Tia Boyd; and Kristine Williams is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

Share This Book