Overview of Transportation Demand Management (TDM)

Urban transportation policy in the early 1970s centers on the efficient use of existing urban transportation systems to accommodate the ever-increasing travel demand without expanding road capacity (Meyer, 1999, p. 575) The focus of Transportation Demand Management (TDM) as a tool to reduce traffic congestion through behavioral change has shifted from addressing air quality concerns regarding Clean Air Act provisions and energy crisis in the 1970s (Giuliano, 1992, p. 328; Meyer, 1999, pp. 575-576), to maximizing traveler choices nowadays (Federal Highway Administration, 2005) by encouraging efficient use of transportation resources (Litman, 2003, p. 245) through public (mandated by the local authority) and private sector (employer-based program) efforts. The general idea of TDM is “any action or set of actions aimed at influencing people's travel behavior in such a way that alternative mobility options are presented and/or congestion is reduced” (Meyer, 1999, p. 576). Besides, Arlington County Commuter Services has provided a list of TDM strategies, including shifting priority away from driving alone, collaborating with employers, improving public transportation, and educating people about their transport options. The first category includes High-occupancy vehicle (HOV) lanes, car-sharing, congestion pricing, and pricing for on-street parking. The second category includes employer-assisted housing, telework, and staggered work hours The third category includes unlimited transit passes, simplified fare structure and payment, and accurate real-time arrival information. The last category includes providing bicycling safety classes, marketing the benefits of ditching cars, and having multimodal awareness events (Arlington County Commuter Services, 2018)

State of knowledge

In TDM policy, travel time savings is the leading factor of behavioral change for new carpoolers, especially during peak hours (Giuliano, 1992). This is to say, the longer the lane, the more time people can save up by using the HOV lane compared to other lanes. However, the benefit of travel time savings is not overwhelmingly attractive to commuters. Giuliano (1992) suggests that activities such as housing errands and needing a car to commute to work that rely on cars are the barrier to encourage people to carpool (p. 331). This case shows the importance of balancing travel time savings and convenience when using TDM to address traffic congestion. The magnitude and success of employer-based TDM program are heavily predicated on the demographics of employees and household activity patterns. For example, Giuliano (1992) points out a survey response regarding participants’ comments on carpool program, including “I need my car before or after work,” and “I need my car during the day at work” (p. 332). However, it is not clear in the second survey response that the need to have a car during the day at work is for business purposes, such as visiting clients, or is for discretionary trips such as buying lunch/running errands during lunch break. To discourage the use of car, TDM policies need to be combined with other corporate policies that reduce business-related activities which rely on a car, in the service of the greater magnitude of success of TDM program. Besides, there is another barrier to implementing an effective employer-based TDM to address traffic congestion. Given that the success of TDM program is predicated on individuals’ household activity patterns and demographics. The higher the turnover rate a company is, the difficult to implement an effective and sustainable TDM program. Company needs to tailor TDM program for their employees if the company’s goal aims to achieve significant impact on behavioral change. Furthermore, Giuliano (1992) points out that TDM policies which aim at the work trip may have relatively small impact by referring to a study that work trip accounts for one-fourth of all trips (p. 334). In this context, TDM policies aiming at work trips nowadays may have even less relative impact given that work trips account for one-six of all trips (see Table 1). Besides, policy recommendations for traffic congestion should include the minimum remote job position requirement to reduce work trips at the regional level. The discretionary trips account for almost half of the trips. Given that there is a body of research on individuals’ attitudes toward different modes of transportation by young and older generation, which shows that it is difficult to change the travel behavior of car-dependent people, would a well-developed MaaS multimodal platform that offers the cheap, frequent, and flexible services aim at discretionary trips change the travel behavior of car-dependent people?

Source: 2017 National Household Travel Survey Statistics

Note. “Vehicle trips are trips by a single privately-operated vehicle (POV), regardless of the number of persons in a vehicle.”

Parking cash-out

The parking cash-out program is an innovative concept proposed by Dr. Donald Soup. The basic idea of the program is to provide employees who give up employer-provided parking benefits in return for a payment that can be used for alternative modes of transportation. California's parking cash-out law can be seen as a pull policy that was enacted in 1992. According to California Air Resources Board (CARB), eight firms that participated in the parking cash-out program had reduced total commuting vehicle emissions by 12 percent, ranging from 5 to 24 percent for the eight firms (California Air Resources Board, 2022).

An assessment on city-level parking cash-out was conducted by Federal Highway Administration (FHWA) to analyze and evaluate the impact of parking cash-out ordinances on congestion, crashes, greenhouse gas emissions, vehicle travel, and equity by setting five single policy scenarios for a sample of nine cities. The study shows that the cash-out impact on projected Vehicle Miles Travelled (VMT) reduction varies from region to region and among scenarios. Still, they all show a significant reduction in VMT. Secondly, daily parking cash-out would result in more VMT reduction given that more people would take daily parking cash-out than monthly cash-out, championed by three surveys regarding cash-out options (Gabriella Abou-Zeid et al., 2023, p. 19)

Source: FHWA, 2023. Organized by Rueichen Tsai

Transportation Demand Management and Environmental Considerations

Given that the emergence of TDM is in response to quality concerns and energy crisis, the discussion of TDM will remain incomplete without addressing TDM in the context of climate change mitigation A study done by Creutzig and He (2009) analyzes the social costs derived from the externalities of car transportation in Beijing and translates the costs into a monetary value which is equivalent to 7.5 – 15 percent of 2008 Beijing’s GDP. Creutzig and He (2009) identify five major social costs, including air pollution, climate change, noise, congestion, and traffic accidents, then derive the monetary value of the costs through different equations. For example, health costs caused by particulate matter 10 associated with lung cancer, chronic bronchitis, and asthma is used for calculating external costs of air pollution. Fuel consumption and damage costs of GHG emissions are used to calculate climate change's social costs Health costs at 69db(A) is used for calculating the social costs of noise. Value-of-time lost in car congestion and the lost in bus transportation caused by car congestion is used for calculating the social costs of congestion. As for the social costs for traffic accidents, they derive the cost from the values of life and severe injuries combined then subtract 75 percent of the costs given that insurances cover three quarters of the costs. The road pricing for traffic congestion they posit is that “[n]eglecting effects on other externalities, an congestion charge of about 1 RMB/km would reduce social costs of congestion by 11 billion RMB a year while constituting opportunity costs for car drivers of around 4 billion RMB” (Creutzig & He, 2009, p. 123).

Although the monetary value they derive is promising, there are several questions remain. First, they posit the health costs at noise level 69 db(A) due to cars, however, is there a proven causal relationship between car noise and the health costs associated with noise level at 69 db(A)?

The future of Transportation Demand Management

There is a body of literature posits different strategies to manage travel demand and mitigate traffic congestion such as express reservation system (Kim & Kang, 2011) and tradable network permit (Wada & Akamatsu, 2013) that renders mobility as a commodity, including permit-based with an auction system (Wada & Akamatsu, 2013) and credit-based mobility management (Dogterom, 2017; Lessan & Fu, 2022; Lessan et al., 2020; Yang & Wang, 2011) With permit-based schemes, users can only travel to specific places or during certain time intervals; as for credit-based schemes, users can travel anywhere at any time. The level of convenience of a credit-based scheme is higher, thus offering more flexibility to roadway users However, it has less capability to mitigate traffic congestion than a permitbased scheme as it cannot eliminate congestion by issuing the number of network permits below the network capacity (Wada & Akamatsu, 2013, p. 305). Besides tradable network permit schemes, there are also other concepts and innovative mobility management contribute to mitigating traffic congestion and promoting car-sharing.

Mobility-as-a-Service (MaaS)

Mobility-as-a-Service (MaaS) has gained popularity over the years and has been examined as a tool of TDM. Furthermore MaaS has been implemented across European cities. Farahmand et al. (2021) examine the potential of MaaS influencing the mode choice of employees in the Netherlands and point out that car-sharing and train attributes’ variations are influential factors on employees’ mode choice. In addition, the study indicates that lowincome, young, multimodal commuters are more susceptible than high-income, old, car- dependent employees (p. 1615) Young people are open to change their travel behavior, this can be partially echoed by the study of factors associated with Millennials’ and Older Adults’ travel behavior. Jamal and Newbold (2020) indicate that young adults are more flexible towards travel modes, are ready to adopt modes that can better suit their purpose, and prefer to live areas that can better suit their transport mode choice (pp. 8-12) Farahmand et al. (2021) conclude that MaaS is a promising element in TDM strategies if it implemented with integrated pull and push policies (p. 1629). However, the commuter rail network is more developed in the Netherlands than in the United States, which may influence employees’ travel choice in this study. It seems that it would be difficult to change car-dependent people’s travel behavior, especially people in the older generation, whether in the European or American context.

Downtown Space Reservation System (DSRS)

Downtown Space Reservation System (DSRS) is a hypothetical system similar to congestion pricing, designed by Zhao et al. (2010) to mitigate congestion in the downtown DC area. In short, everyone who wants to get into the downtown area has to make and pay for a reservation. However, low-income drivers and people with special needs have the priority to make reservations early Reservation price varies depending on vehicle types and entry time to the downtown area, but remains fixed during the time interval. Two objectives of this system include optimizing people throughput and revenue generation, by doing so, this system facilitates decision-makers to decide whether to accept or reject a reservation request. Three choices are provided for a driver who does not have a reservation but needs to make a trip to the downtown area, including using public transit, changing the time of travel, and eliminating the travel need (the authors give an example of using online shopping instead shopping at a local store in the downtown area). In addition, the authors also point out several implementation issues, including reserving spaces, early/late/later departure fees, trip cancellation/no-show fees, fairness, and so forth.

MaaS can only maximize its benefits by encouraging using alternative modes with a robust multimodal platform that integrates data from various transportation network companies such as Uber, Lyft, Juno, etc. One challenge to widely adopt MaaS is that who is responsible for data stewardship and is also trusted by ridesharing companies? Despite the challenge, I would suggest policy makers to take the advantage of public-private partnership mechanism to facilitate MaaS platform. There are several data trusts have been implemented by the federal governments, including the Railroad Information Sharing Environment (RISE), National Highway Traffic Safety Administration’s Partnership for Analytics Research in Traffic Safety program (PARTS), and the Federal Aviation Administration’s Aviation Safety Information Analysis & Sharing program (ASIAS). For instance, RISE is a non-regulatory and punitive, voluntary, and data-driven safety public-private partnership which includes railroad stakeholders and FRA to advance railroad safety (Federal Railroad Administration, 2023) Railroad stakeholders voluntarily share data through the Center for Advanced Transportation Technology Laboratory (CATT Lab). CATT Lab is a laboratory responsible for protects, analyzes, aggregates data that shared by railroad stakeholders and FRA, by doing so, stakeholders can collaborate and determine how they want to use the findings provided by RISE to improve railroad safety. Improving railroad safety by stakeholders alone is difficult through individual effort, RISE is the solution to this problem.

As for DSRS, it is an ideal system that prioritizes traffic congestion mitigation by requiring people to make reservations and charge penalty fees for those who violate the reservations rules, such as no-shows and early/late arrival. However, this system may make entering the downtown area less appealing because of its low flexibility and penalty fees. This would further discourage people from making spontaneous or last-minute trips to the downtown area for work-related or leisure purposes. The level of inconvenience of MaaS, DSRS, and complicated network tradable permit schemes that posed to commuters may be may be the runner-up leading factor contributing to reducing traffic congestion.

Policy and planning implications

Interactions among Transportation Demand Management Policies

TDM policies have been labeled as pull and push policies, carrots and sticks, enablers and deterrents. Pull policies refer to encouraging the use of alternative transportation modes, and push policies refer to the practices of discouraging car usage. Habibian and Kermanshah (2011) mention that implementing various TDM policies simultaneously is more effectively than a single policy (p. 1037). They further refer to the concept of multi-instrumentality, which may overcome identified weaknesses through the simultaneous adoption of TDM policies, which pointed out by Vieira et al. (2007, p. 421). As a side note, simultaneous adoption of policies might result in conflicting outcomes, thus, reducing the positive impact that policies aim to achieve in the first place. As in the case of three policies addressing spatial mismatch, Fan (2012) shows that the expected outcomes of policies regarding innercity job creation, poverty deconcentration, and transportation improvements conflict with each other (p. 10) Habibian and Kermanshah (2011) assess the effect of TDM policies based on the criteria (complementarity, additivity, synergy, and perfect substitutability) developed by Mayeres et al. (2003). Using the stated-preference data of 366 car commuters in their synergy function analysis, Habibian and Kermanshah (2011) found that synergy may occur when a specific combination of policies is implemented simultaneously (p. 1041) in the context of the city of Tehran, Poland. These policies include parking costs, increasing fuel costs, and a cordon pricing policy. They point out that the synergy functions of parking pricing-fuel pricing and parking pricing-cordon pricing integrated policies are monotonic in the studied range. It should be noted, the amount of synergies for the above two integrated policies varies between -2 – 25 percent, -2 – 15 percent, respectively. They interpret it that in order to achieve a synergy effect through simultaneous implementation of these policies, each policy need to be implemented greater than a specific level. However, the value of synergy function of fuel cost-cordon pricing integrated policies is negative, which means there is no synergy effect in the studied range. They make three justifications for the no-synergy effect. First, this may result from partial substitutability. Second, this may be result from the insignificancy of these policies. They make the third justification by citing a study done by May et al. (2006) that “synergy is harder to achieve with a single objective, since the instruments, which contribute to it will to some extent duplicate one another in their impacts” (May et al., 2006, p. 326).

Another study examines the impact of TDM policy on the mode choice behavior of car commuters in the city of Tehran also touches upon the effect of pull and push policies. Khaloei and Habibian (2016) indicate that push policies play an important role in the mode choice process while pull policies slightly impact mode choice decisions. Car commuters are more likely to use alternative modes to work as cordon pricing or parking cost increases. Furthermore, cordon pricing is more effective than the parking cost policy (p. 12). It should be noted that the TDM policies’ interactions in the study done by Khaloei and Habibian (2016) complement the study done by Habibian and Kermanshah (2011) Khaloei and Habibian (2016) point out that simultaneous implementation of push policies, increasing cordon pricing and fuel cost discourage commuters from considering motorcycle use (p. 12).

Discussion of the synergistic effect of TDM policies

What is the best combination of TDM policies? Although the integrated policies of both pull (carrots) and push (sticks) are argued to be more effective at discouraging the use of cars (Piatkowski et al., 2019, p. 50) when evaluating the synergetic effects of TDM policies, researchers may get a coflicting result. As shown in the study by Habibian and Kermanshah (2011), there may be no synergistic effect of implementing two integrated push policies (cordon pricing and increasing fuel cost). In addition, there may be no synergistic effect of implementing integrated push-pull policies (Pricing of automobile travel-Telecommuting incentives-Transit improvements) neither, as shown in the study done by Shiftan and Suhrbier (2002). The intention in pursuit of synergistic effects of TDM policies package to reduce congestion and improve air quality should be applauded. In addition, there are cobenefits of TDM policies. However, planners should be aware of the inconveniences and externalities that may pose to vulnerable groups and lower-income workers/families. To make an analogy between SB 743 and TDM, SB 743 requires using VMT instead of level-ofservice for traffic impact measurement because it takes environmental ramification into consideration. In this case, what other transport metrics should we use that can help us achieve a better synergistic effect of TDM policies while also addressing social equity/inclusion?

Analytical model framework: Activity-based model on travel demand analysis

There are many analytical methods that analyze the effects of TDM policies on environmental factors and VMT reduction. Shiftan and Suhrbier (2002) apply Portland’s activity-based transportation models and household sample enumeration techniques to assess and evaluate three travel demand management policies individually and in combination (integration of three policies) by using data from the Portland, Oregon metropolitan area. In general, the activity-based model has a better prediction of mode choice behavior than the four-step model the problem of uncertainty of travel demand models limits our ability to evaluate transportation policies correctly, thus, deterring our ability to prioritize TDM policies (Shiftan & Suhrbier, 2002, p. 146). They point out several advantages of using an activity-based model rather than four-step model to analyze travel and emission impacts of TDM. First, the activity-based model has a better prediction of traveler responses to TDM policies given its ability to consider the secondary effects of TDM policies. Second, the activity-based model can better consider induced travel, including the generation of new trips resulting from transportation improvement, and trade-offs among various travel behavior decisions. Shiftan and Suhrbier (2002) found that the effect of implementing a combined policy can be stronger or weaker than the effect of implementing the individual policy. Values under the “Combined” column in table 2 are not entirely consistent with the sum of the values from “Policy 1” to “Policy 3.” Which is to say, “it is not possible to know a priori the direction of the combined effect” (Shiftan & Suhrbier, 2002, p. 161).