Travel Demand Management Research Articles

Designing a carbon-trading incentive scheme for mode shifts in multi-modal transport systems

The pressing need to reduce greenhouse gas emissions triggers the imperative for efficient travel demand management. Previous studies have explored budget-based and aggregated incentive programs, which place a significant financial burden on governments and tend to be limited in contributing to effective behavior change in practice due to budget issues. This study proposes a personal carbon trading travel incentive (PCTTI) mechanism, to encourage private car commuters shifting to using public transit. The incentive budget for PCTTI is sourced from the revenue generated through selling carbon emission reductions resulting from commuters’ travel mode shifts. To determine the optimal incentives, we developed an incentive scheme optimization model based on the Stackelberg game model. Numerical analysis reveals the significant potential of the PCTTI to reduce carbon emissions and travel costs across various scenarios within a multi-modal transportation system. This potential is evident amidst changes in the fixed costs of car travel, carbon trading prices, the use of different travel modes, the value of time, and the prevalence of electric vehicles. The advantages are most pronounced when the carbon trading price exceeds 40 CNY/ton, and when the usage of public transit, the value of time, and the proportion of electric vehicles each fall below 0.4, 50 CNY/hour, and 0.4, respectively.

A Study on the Key Factors for the Sustainable Development of Shared Mobility Based on TDM Theory: The Case Study from China

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations.

Assessing Road Users’ Preferences for Various Travel Demand Management Strategies for Adoption in Accra, Ghana

The rise in population density, vehicle ownership, and urban development has significantly influenced travel demand and altered travel patterns, leading to traffic congestion in rapidly growing urban centers such as Accra, Ghana. The traditional approach of expanding roadways to accommodate rising traffic is no longer environmentally sustainable. Instead, the emphasis has shifted toward travel demand management (TDM) strategies as a more sustainable solution. This study aimed to investigate a range of TDM strategies that can be adopted in Accra to improve traffic flow through the lenses of everyday road users. The study employed a questionnaire survey and a stratified sampling technique to gather data from 615 respondents for relative importance index (RII) ranking and Chi-square statistical analysis. The findings revealed that the topmost preferred strategies were mass transit improvements, walking and cycling improvements, and alternative work schedules. Notably, mass transit improvements emerged as the most preferred strategy. Conversely, strategies like efficient parking pricing, congestion pricing, and higher fuel and road taxes for private vehicles ranked lowest, garnering minimal preference. The study also revealed a statistically significant correlation between variables such as age, education level, marital status, income level, and mode of transportation and all the selected TDM preferences. However, no significant relationship was found between gender or car ownership and any of the selected TDM preferences. The study provides valuable insights into road users’ preferences for TDM strategies that can aid in planning future urban mobility systems to proactively manage travel demand, alleviate congestion, and promote sustainable transportation options for the city’s residents.

Jointly modelling activity start time, travel mode, companionship, and destination location choices

Activity-travel decisions, including time use (i.e. activity start time), travel choices (i.e. mode and companionship), and destination location choices are interdependent and complex in nature. They are often controlled by observed factors such as sociodemographic, vehicle ownership, built environment, land use, and unobserved factors like attitude, preference, or habits which typically are not captured in the survey data. To accommodate these complex interactions and capture trade-offs among the time use-travel-land use choices, this study proposes a joint discrete choice model by introducing unobserved factors that are common to them. The model results confirm the presence of complex interdependencies among these choice dimensions. In addition, this study highlights the contribution of observed and unobserved factors introduced in the model by calculating the total variance of utility differences. Lastly, this study provides behavioural insights on the activity-travel patterns, which can be further used to develop robust travel demand management policies.

A theoretic analysis of expressed toll lane pricing policy targeting for a guaranteed level of service

This study compares the first-best toll algorithm that is theoretically appealing with the density-based toll algorithm that is more popular in practice. The comparison shows that both toll schemes can serve as a travel demand management strategy. Although the first-best scheme always leads to minimal total travel time, it is not necessarily attractive from toll operators' perspective. The density-based scheme is (1) more reliable for ensuring the federally mandated minimal level of services when the demand is high, (2) simpler for data collection and implementation, and (3) can generate higher revenues. From the users' perspective, the first-best scheme leads to less total costs in many cases, but the density-based scheme could also lead to less total costs under certain conditions. Traffic patterns could converge in both cases under a day-to-day framework. The speed of convergence for traffic dynamics is faster under the density-based scheme in most cases.

Understanding factors influencing user engagement in incentive-based travel demand management program

Incentive-based travel demand management (IBTDM) has proven effective in mitigating traffic congestion. However, a comprehensive understanding of factors influencing user engagement in IBTDM is lacking due to limited empirical evidence from real-world applications. This study bridges this research gap by examining data from over 4,000 users in an actual IBTDM program, incenTrip, in the Washington, D.C.-Baltimore region. Employing Poisson-Tweedie generalized additive models to account for excess zeros and nonlinear relations, the study examines how home and work-related factors influence users’ enrollment and engagement, measured by the number of registrations, generated trips, and earned incentives at a census block group level. Results reveal that: 1) Urban areas with high population densities and low incomes attract more users and encourage more green travel. 2) Initial enrollment is higher among young, female, Asian, and highly-educated residents, although their subsequent engagement may not be sustained. 3) Workers in educational institutions and retail trades exhibit higher enrollment and maintain stronger engagement than other workers. 4) Well-developed transportation facilities and a higher density of points of interest near the users’ homes or workplaces substantially enhance program attractiveness. 5) Nonlinearities, particularly threshold effects, are observed across various relations analyzed. These findings have valuable policy implications for optimizing ongoing IBTDM programs and informing future initiatives. Policy recommendations include implementing targeted and progressive incentives, adopting combined TDM strategies, prioritizing user-friendly designs, fostering collaboration with employers, and employing nuanced policymaking.

Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior

This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.

An overview of dynamic pricing toll roads in the United States: Pricing algorithms, operation strategies, equity concerns, and funding mechanism

Toll roads with fully dynamic pricing schemes could play a key role in addressing the economic and environmental tolls imposed by increasing congestion levels in major metropolitan areas. Transportation professionals usually argue that they could provide additional revenue streams to accelerate the delivery of much-needed highway projects and serve as a tool for travel demand management. However, the number of dynamic pricing toll roads only increased recently in the U.S., and their performance has not been empirically evaluated systematically. This study provides a comprehensive review of U.S. dynamic pricing toll roads. Pricing is reactive to demand, and additional travel demand management strategies such as incentives for high occupancy vehicles, clean vehicles, and transit are commonly included. Both conventional project delivery methods and private–public partnerships have been used for such projects. Financial statements from a few P3 dynamic toll road projects suggest they are taking full advantage of TIFIA loans and are performing well during their short history. Equity issues are commonly raised, and many projects address them specifically through subsidies to vulnerable groups or re-investment in the community. Further quantitative analyses are needed to test if dynamic toll roads are an efficient tool to address the financial, economic, and environmental challenges major metropolitan areas face.

Money is power: Carpooling stimulus with evidence from an interactive long-term laboratory experiment

Carpooling is one of the travel demand management strategies to mitigate road congestion. Incentive-Based Travel Demand Management (IBTDM) strategies are pivotal for carpool promotion by providing incentives to address inconveniences and privacy apprehensions, yet their efficacy lacks validation. Considering the constraints of incentive budget as well as the penetration rate, it is important to assess the long-term impact of incentives on carpooling, improve the programs in term of information feedback and identify the individuals who are most inclined to carpool. To achieve the goals, we conducted a laboratory experiment through WeChat applet platform focused on carpooling and departure time choices. 194 participants successfully completed the entire experiment. The experiment emphasizes several crucial elements, including long term (participants making daily travel choices in one month), the interactivity and information feedback mechanisms, instruments enhancing participant engagement (redeeming incentives at the end of the experiment, etc.), exit questionnaire to collect travelers' behavioral strategies. The findings elucidate several key insights: 1) Incentives have a significant long-term influence on encouraging travelers to carpool, and compensatory mechanisms for unsuccessful carpool matches further amplify travelers' impetus. 2) IBTDM administrations can promote carpooling by improving feedback information according to travelers' learning effect in each round, such as introducing real-time displays of the numbers of carpool drivers and passengers. 3) Drawing from the analysis of travelers’ participation, behavioral compliance and psychological strategies of carpooling, it can be inferred that individuals with higher incomes, fewer commuting days who have a solid grasp of the experimental mechanism, constitute prospective candidates for carpooling programs. This work contributes to helping IBTDM administrations clarify the target participants and develop a more reasonable carpooling program. The behavioral data collection approach based on our laboratory experiment is portable for future studies.

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Understanding the route choice behavior of metro passenger using the smartphone applications

Understanding the distribution of passenger flow is one of the crucial prerequisites for improving operational efficiency and implementing travel demand management strategies. This paper proposes an improved Random Regret Minimization (i-RRM) model based on the regret theory, where the passenger’s regret on selecting routes is minimized. Considering the growing trend of passengers relying on smartphone apps for route planning and navigation, three factors are investigated, namely, travel time, the number of transfer stations, and the total number of stations traveled. Automatic Fare Collection (AFC) is used to get the proportion of each route, and mobile phone signaling data is used to obtain the ground truth of passengers’ routes proportions for parameter calibration and model validation. The results show that, compared to the ground truth data, the average errors of the proposed i-RRM model, the classic RRM model, and the direct path matching algorithm are 6.03%, 8.96%, and 10.45%, respectively.

Multiscalar trip resilience and metro station-area characteristics: A case study of Hong Kong amid the pandemic

A resilient urban system should cope with the dynamic human activities in its subsystems. Since travel allows people to have their needs satisfied in scattered locales, the dynamics of travel and its effects on people's well-being can vary across different spatial scales (including different subsystems) and over time. However, little empirical research has been done regarding these spatiotemporal variations. In this article, we introduced a trip resilience (TR) index to measure the time-varying travel characteristics, especially trip attraction changes, across different spatial scales. Using empirical data from Hong Kong, we quantified the TR indices for metro trips at a station across three spatial scales: local, neighborhood, and citywide. Then, we examined the spatial distribution of the TR and investigated which station (area) characteristics could explain the stations' TR indices for trips in different scales. We found that the TR indices and their predictors varied across the scales. The diversity of points of interest significantly predicted the TR indices across all the scales. Yet, other characteristics, such as the median age of residents, street density, working population, and provisions of parking spaces, only predicted the TR indices for trips at one or two of the scales. These findings shed light on more refined urban and transport planning strategies and policies concerning travel demand management across spatial scales in the post-COVID-19 era.

Managing network congestion with link-based incentives: A surrogate-based optimization approach

Incentive-based travel demand management (IBTDM) programs endow monetary incentives to encourage travel demand redistribution across space and time. They are more appealing than alternatives such as congestion charging because commuters do not need to pay out of pocket. However, such congestion-alleviation solutions are usually managed by small private companies with constrained incentive budgets. Thus, the incentive should be wisely determined so that a limited incentive budget can be effectively used to fulfill maximum social welfare while maintaining the financial health of the IBTDM program. It is essential to know whether IBTDM is financially sound—that is, whether financial investment in IBTDM will lead to more than the equivalent value in total system travel time reduction. However, optimizing the link-based endowment scheme in a large-scale network is challenging because 1) the objective function and the budget constraint are both characterized by expensive-to-evaluate functions without closed form, and 2) it is a large-scale optimization problem that contains massive amount of decision variables. In this study, a computationally efficient surrogate-based optimization framework that is suitable for high-dimensional problems is proposed. A simulation-based dynamic traffic assignment model is used to evaluate the performance of transportation systems, and a Kriging model with partial least squares acts as the surrogate to approximate the simulation model. The results show that the optimal network-wide link-based incentive scheme improves the performance of the system. The higher the incentive budget, the more effective the incentive and the lower the marginal utility of the incentive. Furthermore, in a well-designed incentive scheme, a $1M investment in IBTDM would lead to much more than the equivalent of $1M in total system travel time reduction, which proves the economic viability of IBTDM and provides support for its promotion. IBTDM implemented within smaller regions and tighter incentive budgets produces higher utility ratios.

Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach

In this study, we aimed to investigate the influence of the built environment (BE) on commuter mode choice using machine learning models in a dense megacity context. We collected 10,150 home-based commuting trips data from Dhaka, Bangladesh. We then utilized three machine learning classifiers to determine the most accurate prediction model for predicting the mode of transportation chosen for commuting in Dhaka. Based on the predictive performance of the classifiers, we identified that the Random Forest (RF) algorithm performed the best. Using the RF model, this study also explored the relative importance of BE factors in predicting commute mode choice, identified nonlinear relationships between the BE factors and mode choice, and examined the interaction effects of these factors on mode selection. Our results reveal that, compared to socio-demographic factors, the BE substantially influence commuter travel behavior. The BE characteristics have a specific nonlinear threshold limit at which they can have a notable impact on lowering private car use, and private car use does not display a constant return of scale with BE. Their interaction effects illustrate the potential optimal combination of BE interventions to lower private car use for commuting. These findings hold substantial implications for urban environmental policy, emphasizing the need for transit-oriented development, travel demand management, and integrated land-use transportation planning to foster low-carbon transportation systems in cities like Dhaka.

Travel demand management strategies to mitigate climate change

As urbanisation intensifies worldwide, the associated increase in travel demand poses significant challenges to mitigate climate change. This study examines the implementation of travel demand management (TDM) strategies within the context of urban development, focusing on the case study of Seoul's green transport zone. Through a comprehensive analysis of transportation, environmental, health, spatial reorganisation and economic aspects, this research study demonstrates the effectiveness of TDM in reducing greenhouse gas emissions and promoting sustainable urban growth. The findings highlight the importance of integrating TDM measures into urban planning efforts to achieve resilient and environmental-friendly transportation systems.

Understanding the attitudes of travelers towards incentive-based travel demand management strategies in Suzhou, China

Incentive-Based Travel Demand Management (IBTDM) strategies utilize monetary incentives to alleviate congestion by encouraging travelers to 1) alter travel routes, 2) shift departure times, 3) shift travel modes, and 4) eliminate trips. In recent years, plenty of IBTDM pilots have been successfully implemented. Since the implementation of IBTDM strategies is subject to incentive budgets and penetration rates, IBTDM administrations should determine how to wisely allocate budgets and how to detect potential participants. Therefore, the primary goal of this study is to discern the relationship between the travelers’ socio-demographic characteristics and their acceptability of various incentive strategies. To achieve this goal, we crafted a questionnaire based on Gaode Map’s implementation experience of IBTDM strategies in Beijing, China. This stated-preference survey was conducted in Suzhou, China, through Gaode Map, with 1,054 potential IBTDM participants participating. The survey results reveal predominantly positive attitudes among travelers toward IBTDM strategies. Notably, travelers exhibit greater receptivity to strategies aimed at shifting travel modes compared to those aimed at shifting departure times. Besides, travelers’ acceptability of IBTDM strategies is significantly influenced by factors such as their preference for public transportation, OD, gender, income, education, and the number of private vehicles. It could be inferred that targeting female college students may be particularly effective, given their high acceptability of IBTDM. These insights offer guidance to administrators for designing targeted strategies and enhancing penetration among the preferred demographic groups in order to maximize the utility of incentive budgets.

Modeling the Peak-Period Bus Commuting Behavior with Staggered Work Hours Using a Regret-Minimizing Learning Method

The staggered work hours (SWH) policy is a practical strategy for managing travel demand, aiming to spread out the temporal distribution of travel volume by adjusting the schedules of travelers’ activities. The influence of the SWH policy on the commuting patterns of passengers using bus transit is not yet clear. We addressed this issue in a many-to-one bus line, treating commuters as Q-learning agents learning to minimize regrets by selecting appropriate bus runs. The learning outcomes reveal a SWH-induced equilibrium, where commuters departing from the same station with the same work start time experience identical minimal commuting costs, regardless of the chosen bus. Subsequently, we investigate the effectiveness of SWH policy by manipulating two key control variables: the division of travel demand between two categories of travelers and the staggered time interval. The results confirm that congestion during peak hours can potentially be mitigated by carefully selecting the above two key parameters. Correspondingly, we provide optimal control boundaries for these two parameters to design an effective SWH policy. Furthermore, we explore the combined impact of physical distancing and SWH policy on traffic flow patterns during an epidemic outbreak. Concurrently, we assess the infection risk through a surrogate index, revealing that the SWH policy has a positive effect in mitigating the risk of contact exposure.

Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership

Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing.

The effect of shared e-scooter programs on modal shift: Evidence from Sweden

Fostering sustainable cities necessitates a significant paradigm shift from motorised vehicles to active mobility. However, the impact of emerging transport modes like e-scooters in this transition remains unclear. To explore the potential of this shift, we polled 805 (non)users of e-scooters in Sweden via an online survey to explore (i) who are e-scooter users and (ii) how e-scooter use affects the probability of modal substitution for users. The propensity score matching method was used to obtain unbiased estimates of e-scooter usage impact on modal substitution and to construct an artificial control group, overcoming potential biases present in previous studies that exclusively surveyed e-scooter users. We found that e-scooter users are more likely to have a high-paying job, a driving license, own an e-bike and car, and public transport cards, suggesting diverse travel behaviours. These findings indicate that e-scooter users are more likely to be highly mobile people with a potential for multimodal transport. Furthermore, being an e-scooter user will increase the probability of shifting their short-range trip to an e-scooter by 46 %. Findings provide pivotal insights into e-scooter modal shifts, crucial for ex-ante and ex-post evaluations of e-scooter adoption, the deployment of e-scooter schemes, and contribute to travel demand management.

Exploring decarbonization pathways for USA passenger and freight mobility

Passenger and freight travel account for 28% of U.S. greenhouse gas (GHG) emissions today. We explore pathways to reduce transportation emissions using NREL’s TEMPO model under bounding assumptions on future travel behavior, technology advancement, and policies. Results show diverse routes to 80% or more well-to-wheel GHG reductions by 2050. Rapid adoption of zero-emission vehicles coupled with a clean electric grid is essential for deep decarbonization; in the median scenario, zero-emission vehicle sales reach 89% for passenger light-duty and 69% for freight trucks by 2030 and 100% sales for both by 2040. Up to 3,000 terawatt-hours of electricity could be needed in 2050 to power plug-in electric vehicles. Increased sustainable biofuel usage is also essential for decarbonizing aviation (10–42 billion gallons needed in 2050) and to support legacy vehicles during the transition. Managing travel demand growth can ease this transition by reducing the need for clean electricity and sustainable fuels.