Travel Demand Management Research Articles

Travel demand management: The solution to public transit congestion? An ex-ante evaluation of staggered work hours schemes for the Paris region

This paper investigates the congestion relief potential of staggered work hours (SWH) schemes for public transit. An ex-ante evaluation framework is developed, which combines a hybrid assignment model with a travel demand management module to simulate the impact of SWH schemes on travel demand and public transit congestion. The key performance indicators capture not only congestion relief benefits, but also rescheduling costs for users. The methodology is applied to the RER A heavy rail line in Paris, the busiest public transit line in Europe. We find that SWH schemes generate congestion relief benefits, as intended, even matching up to telework policies. Yet, such benefits remain moderate, adding up to 20% of total crowding costs for the morning peak period at most. Moreover, substantial rescheduling costs are involved: decreasing the total time standing by 1 h implies shifting fifteen trips by the same amount of time. Regarding policy design, our results suggest that shifting few users by a large amount of time is usually more efficient than shifting many users by a smaller amount of time; the latter may even prove counterproductive by merely transferring the peak to some other time in the morning. We also find for the RER A that focusing the SWH scheme on a single station - the biggest trip attractor - yields benefits similar to applying the scheme over the whole line (for a similar level of effort in terms of total timeshift). The developed framework is intended to provide guidance as how to improve the design of future SWH schemes, in particular in times of social distancing and need for reduced crowding.

Investigating the impact of gender and work profile on attribute importance for mode choice: A Multi-Criteria Decision-Making approach

In the recent years, there has been an increase in research exploring the factors that influence mode choice for commuting. Yet, the impact of gender (female vs. male) and work profile (formal vs. informal) on mode choice for decision-making is not well-known, especially in the Indian context. In this study, a multi-criteria decision-making tool was utilized to bridge this gap, exploring which attributes are most important to respondents from different genders and work backgrounds while choosing a mode of transportation for commuting. Focus group discussions were conducted at six selected locations. Using Fuzzy-AHP (analytical hierarchy process), it was determined that female respondents and formal workers place the greatest importance on travel time and safety, while male respondents and informal workers place the greatest importance on travel cost. In terms of their influence on mode selection, comfort and privacy were the least weighted factors for all four groups. Also, among the four categories, formal workers attributed the significant weight to hygiene and cleanliness (10%). In addition, it was revealed using Kendall's Concordance test that gender and work profiles are associated with the mode of transportation chosen by respondents for commuting. By examining the factors influencing intercity travel for commuting, traffic departments can gain insight into passengers' travel decision structures and develop an effective policy for travel demand management.

On a Flexible Car Use Restriction Policy: Theory and Experiment

Car use restrictions have been adopted in some mega cities that experience rapid car ownership increase and worsening traffic congestion. Although easy to implement and considered fair, most implementations of this travel demand management policy do not offer travelers the flexibility to choose the days that they cannot use their cars. In this paper, we study a flexible car use restriction policy under which a private car cannot be driven on a certain day of a week, but the day can be chosen by its owner. Under this flexible policy, individuals face a dilemma between driving in congestion and traveling without a car, each incurring a cost of its own. The resulting equilibrium solutions under these two competing choices were derived, and a series of laboratory experiments were carried out to validate the theoretical results. The experimental results are found to be in agreement with the theoretical results. Moreover, our analysis shows that the flexible car use restriction policy reduces the average travel cost with a lesser increase in average driving cost when compared with the traditional car use restriction policy. Funding: Z.-Y. Gao was supported by the National Natural Science Foundation of China [Grant 71621001]. W.-X. Wang was supported by the National Natural Science Foundation of China [Grant 71631002]. R. Jiang was supported by the National Natural Science Foundation of China [Grant 71931002]. X.-Y. Sun was supported by the National Natural Science Foundation of China [Grant 71961002]. B.-Y. Zhang was supported by the National Natural Science Foundation of China [Grants 71922004 and 72131003]. X. Han was supported by the National Natural Science Foundation of China [Grant 71801011] and by the China Postdoctoral Science Foundation [Grant 2018M631331]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1200 .

Parking Charges: Ingeniously Effective and Publicly Accepted in Riyadh?

The background of this study is relevant to parking facilities at shopping malls in the Kingdom of Saudi Arabia (KSA), where all parking services are mostly free, with very few exceptions. Demand on parking places at shopping malls and recreational areas in Saudi cities are generally very high. This is partly because most of the entertaining, shopping, and recreational areas are indoors and are typically located at shopping malls with huge parking provision. With the vast increase in car ownership and use in the country over the past few decades, a consequent increase in demand on roads and parking facilities has been observed, which has no doubt resulted in further congestion. This study aims to investigate the willingness to pay to save time searching for a parking place at shopping malls in Riyadh city in the KSA. The methodology includes interviewing shoppers in shopping mall car parks and asking about their search time for a parking space and their willingness to pay to save search time, as well as to report on their socio-economic characteristics including age, gender, employment, education, car ownership, and income. The outcome of this study shows that on average, shoppers spend about 9 min searching for a parking place for their shopping and recreational trips. The results also show that, on average, shoppers reported that they go shopping, eating out, etc., about three times per week, and the average willingness to pay to save time searching for parking space is about 10 Saudi Riyal (about 2.7 USD) per visit. The modelling results show that there is a higher willingness to pay from the middle income and car-ownership categories of the population than from the highest income and car ownership groups. This might reflect the fact that the middle-income groups are much more dependent on themselves to drive their cars and search for a parking space every time, while those of higher incomes could possibly rely on their private drivers most of the times to drive them and park their cars. This paper contributes to the literature by providing a first understanding on how demand for parking facilities is affected by various socio-economic factors and the willingness to pay to save search time at these parking locations. The implications of the outcome of this research will be of use for decision makers and city authorities in planning travel demand management policies and programs that aim at reducing demand on private cars and achieving sustainability.

How mentally healthy we are during the pandemic? Findings from changed travel behavior

BackgroundMany people changed their travel behavior during the coronavirus pandemic with more telecommuting, fewer trip frequencies, and less use of transit and ride-hailing to avoid infection. The lack of outdoor activities may result in social isolation and then trigger anxiety or depressive symptoms. Research objectiveThis study examines the relationship between anxiety and depression, and correlates various sociodemographic, income, job status, health-related factors, and travel behavior changes in six large U.S. cities. DataU.S. Census Household Pulse Survey Phases 3.0 and 3.1 are employed. MethodGAD-2 and PHQ-2 are used to screen the scores of anxiety and depression. The synthetic minority oversampling technique is applied to correct sample distribution. The multivariate mixed model is employed to examine relationships. Results(1) Anxiety and depression are positively correlated, and the percentage of high anxiety is greater than the percentage of high depression. (2) The levels of anxiety and depression significantly vary across the six cities. (3) Women, young, singles, and white people have higher levels of anxiety and depression during the pandemic. (4) People who are willing to receive vaccination tend to have higher levels of anxiety and depression. (5) The prevalence of depressive disorders is significantly lower in the high-income group. (6) People who applied for unemployment insurance and experienced expense difficulties are more likely to suffer high levels of anxiety and depression. (7) Travel behavior changes, measured by increased telecommuting, reduced trip frequency, and reduced use of transit and ride-hailing, all suggest positive correlations with anxiety and depression. ConclusionsMore assistance and attention should be given to women, singles, and low-income households to reduce the prevalence of mental stress in vulnerable groups. Telecommuting can be but need to work with other travel demand management strategies. Travel and outdoor activities should be promoted under the new normal.

Delay: The Next “D” Factor in Travel Behavior?

Empirical research on the impact of congestion on travel behavior remains limited. This study fills this gap using a comprehensive analytical framework, an improved time-related travel delay measure, structure equation modeling, and the disaggregated household survey data for the Puget Sound Region. The results indicate that travel time delay is associated with fewer household vehicles, fewer vehicle trips, and lower vehicle miles traveled. The findings confirm the intricate impacts of the travel time delay, reinforce the importance of the “D” factors in travel behavior, and point to the need for comprehensive solutions to travel demand management.

Kerbside Parking Assessment Using a Simulation Modelling Approach for Infrastructure Planning—A Metropolitan City Case Study

The main purpose of this research is to investigate the effect of kerbside parking demand and provision on short-term parking (STP) and freight activity space (FAS) as a benchmark for infrastructure planning, considering the impacts of expected future growth and capacity changes. In this study, we adopted a mixed-methods approach of quantitative analysis including a spatial view of parking using manual and video-captured camera data from the majority of STP and FAS parking bays covering a diverse range of loads/tasks with different levels of elasticity and substitutes, as well as simulation of current demand influenced by various factors, as a basis for the development of strategies and prioritisation of the allocation of limited kerbside spaces in Parramatta, a rapidly transforming/growing CBD city centre environment. Parking demand consisted of a diverse range of FAS and STP categories. Spatial analysis showed a non-homogeneous distribution of parking demand and loads across several sections of the city. A large proportion of short-term parking spaces is attributed to two peak periods during the day and increased traffic volumes at peak times. Comparatively lower average parking times in the northern and western regions compared to those in the city centre indicate the potential to reduce peak parking periods and therefore traffic congestion in the city centre by changing parking limits. The presented simulation model can be used as a reliable benchmarking model for the simulation of future impact scenarios and to make recommendations with respect to infrastructure planning and to develop travel demand management strategies. This research is based on a case study and is therefore subject to limitations in its applications in other contexts. Extension of the baseline simulation with future impact scenarios is planned for the next stage of this research. A simulation model is presented and illustrated as a reliable benchmarking tool for the simulation of future impact scenarios through a case study of a rapidly changing city environment.

How do soft factors influence take-up of gamified travel demand management schemes in public transport: An investigation in Gold Coast, Australia

There are various factors that have impact on the effects of the Travel Demand Management (TDM) schemes. This paper investigates the role of soft factors (e.g., participants' perceptions and attitudes) in travellers' intentions to participate in a public transport orientated TDM scheme. A multinominal logit model is analysed giving a key result suggesting that successful implementation of gamified and incentive based TDM schemes must pay attention to satisfy a traveller's perceptions of enjoyment and usefulness. While the findings reinforce the role of ‘practical’ decision making (i.e., ease-of-use and usefulness) for the traveller in participating in a gamified TDM scheme, they also reveal that ‘fun’ or ‘exciting’ (i.e., enjoyment) also play an important role to make the scheme more attractive, particularly for public transport users. Model results also reveal that how easy the game is remains critical to the participant's intention to join and play. In particular, an easy game increases the intention to join for educational users. In concluding, the results show that participants' perceptions and attitudes are critical not only in the design of an effective gamified scheme but also in identifying the target groups (e.g., students or commuters) for the proposed schemes.

Modal shift behavior of car users to light rail transit, some evidence from the field

Growing population and car dependency in developing countries have led to congestion that adversely affects the environment, travel time, trip cost, accidents, and public transportation reliability. Through implementation of travel demand management (TDM) policies, governments and policymakers aim to reduce private vehicle dependency and encourage people to use public transportation. Light rail transit is an important part of an attractive public transport. Rail transportation systems, though offer many potential benefits, are a major financial challenge for governments because of the high capital and operation costs. Therefore, passenger behavior must be determined before a new system is introduced. Using a stated preference (SP) questionnaire, private car users' behavior in Tehran's universities has been investigated to determine the explanatory factors affecting the modal shift to urban light rail transit (LRT). A binary logit model estimation results showed that men are less likely than women to shift toward LRT because they rely more on their private cars. It was the provision of free on-street parking at destinations and the frequency of using private cars on a weekly basis that had the most negative effect on LRT modal shift, demonstrating the critical role that implementation of TDM policies could play. Moreover, reduction in travel time by LRT has the most positive impact on modal shift toward LRT among private car users. As an interesting finding, marginal effect values indicate that a 10% reduction in travel time (0.32) has a greater impact than the possibility of sitting 50% of the travel time (0.25) on the likelihood of modal shift to LRT. Furthermore, the probability of modal shift to LRT will be reduced by 0.12 units for each unit increase in car ownership. In addition, owning a driver's license was also negatively correlated with LRT modal shift and decreased the likelihood by 0.27. This research will facilitate the decision-making and planning for future transportation systems to increase LRT's utility for potential users.

Travel Demand Management Implications during the COVID-19 Pandemic: The Case Study of Tehran

The COVID-19 pandemic has had an unprecedented impact on the transportation sector, with changes expected to last for a long time. Following the reopening phases, increased use of private vehicles and continued avoidance of exposure to the infectious virus in public transportation exacerbated urban traffic and environmental problems. The paper aims to evaluate the impact of travel demand management strategies during the COVID-19 pandemic in Tehran, Iran. That city provided a unique case study because of its high COVID-19 contagion and death rates, traffic congestion, and lack of government restriction plans, which are common in developing countries. Four scenarios combining various travel demand management strategies were considered during the pandemic. The findings highlight the importance of considering the tradeoffs between different performance measures when selecting a suitable travel demand management strategy. Depending on the goals that decision-makers have set, the strategies may not be additive. While teleworking, e-learning, and rescheduling working hours all help reduce traffic congestion, the pricing strategy contributes significantly to meeting environmental and health goals. The findings also highlight the nonlinear pattern of change associated with each strategy.

A discrete-event public transportation simulation model to evaluate travel demand management impacts on waiting times and crowding conditions

Several approaches have been proposed and adopted by researchers and decision-makers to improve and deal with public transport operation issues, especially travel demand management (TDM) measures. Disruptions like lockdowns provoked by weather conditions, political riots, special events, natural disaster issues, or the recent COVID-19 pandemic create a need for tools to manage public transport demand and supply o keep users circulating in an efficient, convenient and safe manner. Our work develops a simulation tool of the operations of a public transport system using smart card, GTFS and census data to evaluate the impacts of different intervention scenarios using the pandemic context as a case study. Using a pre-pandemic baseline scenario, we study the impact of several travel demand and public transport supply measures, focusing the analysis on waiting times and crowding conditions inside vehicles and platforms. As a result, we generate easy-to-analyze visual outputs that facilitate prioritizing actions at the metropolitan and district level, identifying where and when waiting times and crowding conditions would exceed certain thresholds.

Analyzing Potential Impacts of Motorcycle Travel Demand Management Using an Activity-Based Travel Demand Model for Ho Chi Minh City, Vietnam

This paper analyses the potential impacts of motorcycle travel demand management strategies for Ho Chi Minh city, a motorcycle dominant city of Vietnam. Four scenarios on managing the use of motorcycle are analyzed using an activity-based travel demand model called FEATHERS-HCMC. The model is adapted from the FEATHERS model developed for Flanders, Belgium and based on the household travel survey in Ho Chi Minh City. The modeling framework of FEATHERS-HCMC is a combination of the heuristic rules and the econometric models. The simulation results suggest that a strong enforcement on motorcycle use would significantly reduce motorcycle trips and induce the rearrangement of location and time-of-day for daily activities. These results provide empirical evidence that shows several advantages of using activity-based travel demand models comparing to the current four-step models. FEATHERS-HCMC is a promising tool to support decision-making process for assessing sustainable transportation policies, not only for HCMC but for other regions with similar characteristics.

Impact of telecommuting on Japanese Citizen's travel, activities, and residential locations: Experiences and future expectations under COVID-19 pandemic

In the past few years, telecommuting has diffused on a large scale across many developed countries; however, its popularity in Japan is low due to various factors and cultural differences. The outbreak of COVID-19 has made the adoption of telecommuting indispensable, reaching an unprecedented level in Japan, bringing dramatic changes in the lifestyles and behaviors of citizens. This study investigates how and to what extent this pandemic-induced telecommuting affected employees’ travel, activities, and residence locations and explores their expectations of post-pandemic life. The data were collected through the online questionnaire survey across three major metropolitan areas in Japan that incorporates a comprehensive set of questions regarding telecommuting implementation and feasibility, lifestyles before and during the pandemic, and expected way of life after the pandemic, and the total of 2045 respondents were collected. The analysis has been done based on revealed preference data considering two time periods i.e., before COVID-19, during COVID-19, and for the post-COVID 19 scenario where stated preferences are considered. In the first step, telecommuting behavior, telecommuting preferences and plans, and activity patterns are estimated based on aggregate sample data. Next, by using the stated preference survey conducted, ordered probit model and mixed logit model are applied to identify factors influencing post-pandemic telecommuting frequency and residential location preference, respectively. The analysis revealed two major findings: telecommuting brought significant changes in multiple aspects of people’s lifestyles and activities that might persist after the pandemic; although determinants that govern telecommuting frequency and residential location choice after COVID-19 may differ across metropolitan areas, commuting/telecommuting frequency is the prime factor influencing residential location decisions. The findings of this research can guide transportation and urban planners in establishing effective travel demand management tools and sustainable urban networks for the new-normal era.

Breaking commuter mode-use habits: An exploration of deliberative decision-making windows and their implications for travel demand management

It is widely accepted that travel behaviour can be habitual. It is also widely accepted that voluntary Travel Demand Management (TDM) seldom records city-wide mode-switching impacts. If travel choices are habitual, this is unsurprising, as much of the population are not making deliberative choices, and new information is not considered. Shifts to deliberative choice-making are posited to occur when a ‘stressor’ (e.g., changing job location) is experienced, which renders the travel habit no longer satisfactory. A common conceptualisation of these moments is that the stressor triggers deliberation, manifested in information-seeking and experimentation, to find a new satisfactory behaviour. If found and implemented, a new habit may be formed. This conceptualisation has implications for improving TDM impacts. It is tempting to assume that voluntary TDM measures should target those who have just experienced such stressors, as they are likely to be most receptive to deliberative change. This paper reports the findings of a retrospective survey of a purposive non-probability sample of (n = 250) Cape Town commuters who had experienced a habit-breaking stressor. A recall aid in the form of an ‘event history calendar’ was used to help create multiple memory recollection pathways of past commuting behaviour, and a ‘deliberation calendar’ was used to guide respondents in reporting the process of habit breaking. The study found that the trigger for deliberation and information-seeking was not the manifestation of a new residence or job location (together accounting for the majority of observed stressors), but around two months earlier when the decision-maker consolidated a plan of action. This finding contributes to a growing literature on the temporal dimensions of behaviour dynamics, and has implications for how TDM should be targeted. Targeting new homeowners or employees misses the ‘window of opportunity’ to influence deliberative decisions and new habits. These decision-makers need to be targeted sooner, while still house- or job-seekers.

Booking cum rationing strategy for equitable travel demand management in road networks

Trip booking and traffic rationing have been proposed as two alternative travel demand management (TDM) strategies over the last two decades. Through artificially restricting demand (vehicle travel) by booking or rationing the scarce road capacity during the peak periods, the negative externalities generated by travel demand over available supply or road capacity can be reduced. In many cases, the two strategies also have the main goal of reduction of air pollution. Trip booking system allows vehicles/drivers to reserve prescribed areas or some lanes/segments of freeways/roads for their use during specific time periods, thereby maintaining a certain level of service of the roadway space. It is often in the form of a permit to control the number of reservations issued. On the other hand, traffic rationing is often achieved in reality through restricting access into an urban cordoned-off area or city center based on the last digits of the license number on pre-established days and during certain periods, usually the peak hours. However, theoretical studies and practical implementation of the two strategies have been conducted separately. Taking their advantages, this paper proposes and demonstrates a novel hybrid strategy of booking cum rationing for efficient and equitable TDM. We show that the problem of interest can be simply formulated as a convenient linear programming problem in a general network. Simple examples are provided to elucidate how this hybrid strategy can achieve a traffic flow distribution pattern prescribed by the traffic planner while maintaining the fairness, efficiency, and flexibility of individual choices.

Pricing schemes of simultaneous optimization of congestion and air pollution: a case study in Iran.

The problem of traffic congestion and the environmental issues related to air pollution are among the essential problems of urban management that metropolitan cities are trying to mitigate. Given that the contribution of motor vehicles to air pollution is significant, both goals are achieved by managing urban transport. Among the various methods of travel demand management, congestion pricing is a very efficient measure. This study tried to simultaneously increase the efficiency of the transportation network and reduce the environmental effects by using a bi-level model for the multi-modal network. For this purpose, the upper-level model minimizes the objective function, i.e., pollution emission costs and overall commuting costs. The lower level also has a transportation network model that provides the condition of user equilibrium. The genetic and Frank-Wolfe algorithms have been used to solve the bi-level programming model. Two pricing schemes, cordon-based and link-based, are used to investigate and assist policymakers. The proposed algorithm is also applied to a real-world road network in Isfahan, Iran. The results of the proposed models for different pricing strategies were compared. According to the results, both pricing schemes mitigate traffic congestion and pollution, although the reduction in pollution outside the cordon is less than inside. Demand has also shifted from the private car mode to public transportation by an average of 15%. However, link-based pricing provides better performance than cordon-based pricing. This study indicated that a higher total collected toll in link-based pricing is accompanied by a sharper reduction in congestion and pollution mitigation, which can be spent on alternative facilities and infrastructure by the municipality, such as the development of public transportation and parking.

Travel demand management policies: A case study of Singapore and transferability potentials for Hanoi

This research study investigates the travel demand management (TDM) strategies and solutions and associated policies through a case study of Singapore to gain knowledge and experiences, and to evaluate the potentials for policy transfer in TDM from Singapore to Hanoi, the Capital of Vietnam. The research follows a qualitative approach that critically analyzes policies from international perspectives for lesson-drawing. The ideas generated in the literature review are reinforced by the case study of Singapore that examines relevant issues to acquire insights into policy transfer initiatives in TDM. The research identifies considerable differences between Singapore and Hanoi with respect to the development objectives, social and political settings and the governance structure of the city in general, and in transport sector in particular. The results show that there are critical political, institutional, physical and social constraints on policy transfer from Singapore to Hanoi. Given the stated critical constraints, it could be premature to conduct transfer of TDM policies from Singapore to Hanoi for the time being, but the findings from the case study have significant policy implications to Hanoi, and lesson-drawing is highly valuable and attainable. An initial screening was conducted to evaluate the applicability of policies used in Singapore as a starting point to develop a comprehensive TDM framework for Hanoi. The findings of the case study in critical contextual constraints for policy transfer should be useful in developing a roadmap for the transfer of TDM policies to a growing city like Hanoi.

Time-dependent pricing strategies for metro lines considering peak avoidance behaviour of commuters

With growing concerns about travel demand management practices in overcrowded metro systems, it is considered that time-dependent pricing strategies are effective for dealing with the crowding occurring during peak commuting hours. In this study, a bi-level optimisation framework is used to consider the peak avoidance behaviour of commuters in the development of time-dependent pricing strategies. The behavioural sensitivity of commuters to pricing factors is investigated in terms of departure time and mode shift decisions based on a stated preference survey conducted in Beijing, China. The proposed bi-level programming model comprises a multi-objective optimisation model at the upper level and a nested logit-based stochastic user equilibrium model at the lower level. Based on an empirical case study of the Batong line in Beijing metro, nine optimal time-dependent pricing strategies are tailored by representative decision preferences, yielding up to 13.97% decrease in the peak ridership during rush hours.

Demand management for smart transportation: A review

The current revolutions of automation, electrification, and sharing are reshaping the way we travel, with broad implications for future mobility management. While much uncertainty remains about how these disruptive technologies would exactly impact demand for future mobility and enhancement of transportation supply, it is clear that innovative demand management is equally important as smart supply technology development in solving worsening traffic problems in big cities. In this work, we will discuss the significances, opportunities, and challenges of demand management in the era of smart transportation. Innovative ways of travel demand management for road transportation, public transit, and smart mobility are described, including tradable travel credit schemes for road congestion mitigation, revenue-preserving and Pareto-improving strategies for peak-hour transit demand management, and a novel reward scheme integrated with surge pricing in a ride-sourcing market.

Revisit employer-based travel demand management: A longitudinal analysis

Employer-based travel demand management (TDM) programs have been implemented worldwide for decades, but rarely are longitudinal analyses implemented. This study utilizes a longitudinal dataset to assess the effectiveness of TDM measures on vehicle trip rates (VTRs) over time. The results suggest: (1) VTR grows over time, and TDM measures may decelerate the growth but cannot reverse the trend; (2) for organizational tools, collective bargaining is negatively associated with VTR; (3) distributing transit passes is negatively associated with VTR; (4) ride match is positively correlated with VTR; (5) guaranteed ride home measures, including emergency rides and employer-provided vehicles, are positively associated with VTR; (6) subsidies promoting multimodal transportation (transit, walking, biking) incentivize vehicle trip reduction; the more subsidies, the lower VTR, and such effects are reinforced over time; (7) in contrast, subsidies promoting carsharing are associated with more vehicle trips; (8) larger companies and areas with higher job densities have a lower VTR. To inform practice, when rewarding employees, distributing transit passes is a preferred strategy. Collective bargaining builds agreements between employers and employees and helps promote trip reduction. Carpooling-related and guaranteed ride-home measures should be applied with caution.