Departure Time Choice Research Articles

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees’ parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees’ trip scheduling, employer’s EP investment decision, and commercial parking operator’s pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees’ departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer’s optimal EP investment decision. A competitive game between employer’s investment decision and commercial parking operator’s parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Optimizing dedicated lanes and tolling schemes for connected and autonomous vehicles to address bottleneck congestion considering morning commuter departure choices

The introduction of connected and autonomous vehicles (CAVs) provides a significant opportunity to address the persistently increasing problem of urban traffic congestion. By virtue of their connectivity and automation features, CAVs can reduce vehicle headways, thereby increasing road capacity and enhancing throughput. It has been hypothesized that CAV-infrastructure design policies can influence traveler behavior in ways that could reduce congestion. This research focuses on the potential of using CAV-dedicated lanes (CAVL) to alleviate traffic congestion in a bottleneck corridor that serves both human-driven vehicles (HDVs) and CAVs. We delve into investigating the impacts of CAVLs on the departure time and lane choices of morning commuters. The study first expresses traffic equilibrium conditions as a linear program with complementarity constraints. Then, a system-optimal commute congestion management design is formulated to minimize the overall system cost, which consists of queuing delays and early and late arrival costs. The results of the computational experiments suggest that: (i) the CAV technological advancements can significantly reduce traffic congestion under CAVL deployment with an almost similar effect as a tolling policy; and (ii) the lower value of time for CAV commuters leads them to depart closer to their desired arrival time without a tolling policy, which could significantly increase the bottleneck traffic congestion that commuters experience, particularly HDVs.

A Paradox of Telecommuting and Staggered Work Hours in the Bottleneck Model

We study the long- and short-term effects of telecommuting (TLC), staggered work hours (SWH), and their combined scheme on peak-period congestion and location patterns. In order to enable a unified comparison of the schemes’ long- and short-term effects, we develop a novel equilibrium analysis approach that consistently synthesizes the long-term equilibrium (location and percentage of telecommuting choice) and short-term equilibrium (preferred arrival time and departure time choice). By exploiting their special mathematical structures similar to optimal transport problems, we derive the closed-form solution to the long- and short-term equilibrium while explicitly considering their interaction. These closed-form solutions elucidate the discrepancies between the effects of each scheme and uncover a paradoxical finding: the introduction of SWH, in conjunction with TLC, may increase the total commuting costs compared with the scenario with only TLC, without yielding any improvement in worker utility. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), the 3rd period of SIP “Smart Infrastructure Management System” [Grant JPJ012187] (Funding agency: PublicWorks Research Institute, Japan). This work was also supported by Japan Society for the Promotion of Science (JSPS) KAKENHI [Grants JP20J21744, JP21H01448, JP24K00999, JP20K14843, and JP23K13418] and the Support Program for Urban Studies of the Obayashi Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0520 .

Estimation of schedule preference and crowding perception in urban rail corridor commuting: An inverse optimization method

This paper introduces an inverse optimization method to uncover commuters’ schedule preference and crowding perception based on aggregated observations from smart card data for an urban rail corridor system. The assessment of time-of-use preferences typically involves the use of econometric models of discrete choice based on detailed travel survey data. However, discrete choice models often struggle with potential endogeneity issues in behavioral observations when estimating individual samples from massive transit data with limited exogenous identifying information. This motivates us to employ an equilibrium modeling approach to capture the dynamism hidden in commuters’ departure time decision-making from aggregations. Assuming user optimality in observed choices, an inverse optimization method is proposed to find a set of preference parameters in the stochastic user equilibrium-based morning commuting model with heterogeneous commuters so that the resulting equilibrium pattern best approximates the observed departure rate distribution over time. The proposed inverse optimization problem can be formulated by a bi-level programming model and a sensitivity analysis-based solution framework is further designed for model estimation. Lastly, the smart card data and train timetable data from the rail corridor along the Beijing Subway Batong Line are synthesized for a case study to estimate commuters’ departure time choice preferences during morning peak periods, as well as to validate the robustness and practicality of the proposed method.

Maximin headway control of automated vehicles for system optimal dynamic traffic assignment in general networks

This study develops the headway control framework in a fully automated road network, as we believe headway of Automated Vehicles (AVs) is another influencing factor to traffic dynamics in addition to conventional vehicle behaviors (e.g. route and departure time choices). Specifically, we aim to search for the optimal time headway between AVs on each link that achieves the network-wide system optimal dynamic traffic assignment (SO-DTA). To this end, the headway-dependent fundamental diagram (HFD) and headway-dependent double queue model (HDQ) are developed to model the effect of dynamic headway on roads, and a dynamic network model is built. It is rigorously proved that the minimum headway could always achieve SO-DTA, yet the optimal headway is non-unique. Motivated by these two findings, this study defines a novel concept of maximin headway, which is the largest headway that still achieves SO-DTA in the network. Mathematical properties regarding maximin headway are analyzed and an efficient solution algorithm is developed. Numerical experiments on both a small and large network verify the effectiveness of the maximin headway control framework as well as the properties of maximin headway. This study sheds light on deriving the desired solution among the non-unique solutions in SO-DTA and provides implications regarding the safety margin of AVs under SO-DTA.

A review of air pollution exposure impacts on travel behaviour and way forward

Travel is an activity that everyone carries out in their daily life. While travelling, commuters are exposed to air pollution, which is likely to impact their mode, route, and/or departure time choice. Various review studies on vehicular emissions have been conducted in the past, but the aspects of its impact on resulting air quality and travel behaviour are sporadic and largely unorganized. In an effort to close this gap, this study carried out an extensive review of 63 studies to document the influence of air pollution on travel behaviour. The findings reveal that a majority of the research in this arena has been carried out in the USA and China, and the highest instances of behaviour change was observed when users shifted their routes to reduce exposure. The awareness about AQI and its real-time pre-trip information plays a vital role in lowering commuters’ air pollution exposure. Finally, strategies such as congestion/emission pricing schemes, MaaS, etc. could provide a clean travel environment to all commuters by reducing their exposure during travel.

Evacuation Efficiency under Different Departure Time and Destination Choice Preferences

Evacuation Efficiency under Different Departure Time and Destination Choice Preferences

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.

Analysis of multi-modal public transportation system performance under metro disruptions: A dynamic resilience assessment framework

The well-functioning multi-modal public transportation systems play crucial roles in reducing traffic congestion, alleviating environmental pollution, and improving mobility. There are variants of disruptions that may impede the smooth functioning of public transportation systems and challenge their normal operations. It is essential to develop resilient multi-modal public transportation systems, and a reasonable resilience assessment framework serves as the foundation for developing such systems. In this paper, we propose a resilience assessment framework based on performance-based measurement metrics. We develop a dynamic simulation procedure that considers the impacts of disruptions on passenger choices of path and departure time. By incorporating these features into the simulation procedure, we can effectively model the variations of passenger flow under disruptions. Finally, to demonstrate the proposed resilience assessment framework and dynamic simulation procedure, the multi-modal public transportation system in Beijing is considered as a case study. The results reveal that the system exhibits good resistance and recovery capabilities, but it lacks robustness. Additionally, we discuss the effects of downtime, bus frequency, bus bridging services, and proportions of commuters on resilience, which lead to several valuable policy implications.

Travel behavior and system dynamics in a simple gamified automated multimodal network

Automated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike‐Sharing

The Issue of Subway Commuters’ Departure Time Choices under the Influence of Bike‐Sharing

Sequentially modeling household accommodation, destination, and departure time choices

During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.

Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area

ABSTRACT Pre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students.

Experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions

ABSTRACT This paper presents an experimental study on the departure time choice behavior during the morning rush hours under different road capacity conditions. Experimental data are analyzed from the aspects of the equilibrium state of a traffic system and the choice behavior of subjects. The experimental results showed that the user equilibrium is easy to achieve in the medium-capacity scenario; however, it is difficult in the low- and high-capacity scenario. This implies that the user equilibrium cannot predict the aggregate behavior well when the bottleneck capacity is too low or too high. A reinforcement learning model is constructed to reproduce experimental results and uncover subjects’ learning mechanism. Simulation results are in good agreement with the experimental results. The results presented in this study could provide the theoretical support for developing measures for transportation management and control during the morning rush hours.

Hysteresis and the Unobserved Congestion Branch in the Macroscopic Fundamental Diagram: Theoretical Considerations and Modeling

The macroscopic fundamental diagram (MFD) is developed to describe traffic operations aggregated over an area. The MFD is defined by network traffic states as a relationship between the accumulation of vehicles and flow or speed of vehicles. The concept of the MFD has been applied to model traffic dynamics and to design control strategies. For various applications (e.g., routing and departure time choices), the MFD is often assumed to be of a particular shape, consisting of two branches—a free-flow branch and a congestion branch. However, empirical observations show some inconsistencies between the theoretical and the empirical MFD. First, the empirical MFD only presents free-flow branch, i.e., the congestion branch is missing. Second, the MFD presents as hysteresis loop(s). This paper explores these differences, providing insights into urban network traffic dynamics. This work takes the travelers’ departure time choices and user equilibrium (UE) as starting points. We consider demand to be in UE in terms of departure time choices. Using this property of the demand profile, the paper proposes a closed-form expression for average density and outflow. Finally, we show some insights in the urban traffic dynamics: (i) an explanation of the hysteresis phenomenon solely from the perspective of departure time choices and UE and (ii) an explanation of why we hardly observe the MFD congestion branch in real life even in heavily congested networks. Our study shows that, for management purposes, the missing of congestion branch is a result of UE, rather than an indicator of congestion severity.

Dynamic equilibrium analyses in a ride-sourcing market under travel time uncertainty

This paper proposes a dynamic equilibrium framework for the morning commute problem in the ride-sourcing market under travel time uncertainty. It is formulated as a simultaneous departure time and mode choice problem (SDTMCP) with random travel delay. Commuters are considered to choose among four travel modes: (1) driving alone, (2) taking non-shared service, (3) taking ride-sharing service, and (4) taking the metro. The equilibrium state is reached when no commuter can reduce their expected travel cost by unilaterally changing their mode or departure time. We derive the dynamic equilibrium queuing patterns analytically under certain assumptions, and develop an approach using nonlinear complementarity problem (NCP) to solve general problems where analytical results cannot be readily derived. We further explore the pricing strategy of the ride-sourcing platform, in which travel time uncertainty and the prices of ride-sourcing services jointly determine the profit performance. A mathematical program with equilibrium constraints (MPEC) is formulated to search for the optimal service prices to achieve the maximum profit. The framework is then extended to investigate the pricing strategy in a two-sided ride-sourcing market and examine the metro crowdedness effect in the numerical studies.

How do pre-event activities alleviate congestion and increase attendees’ travel utility and the venue's profit during a special event?

This study aims to investigate attendees’ trips to venues before special events considering pre-event activities. We focus on how pre-event activities change attendees’ departure-time choices and affect traffic congestion near a venue. By describing attendees’ pre-event utility and the attractiveness of the venue, a bottleneck model is proposed to model attendees’ departure times with heterogeneous pre-event utility. Attendees’ heterogeneity in pre-event utility is depicted by a continuous distribution of pre-event utility sensitivity, which changes with the attractiveness (e.g., the overall price level, facility, or service levels) of the venue. Different distributions are adopted to depict the attendees’ pre-event utility sensitivity and are further used to analyse the equilibrium at the bottleneck. The conditions to eliminate the queue at the bottleneck are determined, which are related to the distribution of the pre-event utility sensitivity of the attendees. We further analyse the impact of the venue's attractiveness to attendees on the distribution of attendees’ pre-event utility sensitivity. The optimal choice of the overall price level and facility or service level is determined to maximise the profit of the venue and the total trip utilities of all attendees. Finally, numerical examples are conducted to illustrate the equilibrium at the bottleneck and examine the no-queue condition and the conditions to maximise total trip utility and venues’ profit.

Perimeter control in a mixed bimodal bathtub model

Perimeter control involves monitoring network-wide traffic and regulating traffic inflow to alleviate hypercongestion. Implementation of transit priority with perimeter control measures, which allow transit into a controlled area without queuing at the perimeter boundary, is an effective strategy in bimodal transportation systems. However, travelers’ behavior changes in response to perimeter control strategies, such as shifts in their departure times and transportation modes, have not been fully investigated. Therefore, important questions remain, such as the use of transit during perimeter control with transit priority. This paper examines the travelers’ behavior changes in response to perimeter control with transit priority in a mixed bimodal transportation system with cars and flexible route transit (FRT) vehicles. We model departure time and transportation mode choices in such a transportation system with hypercongestion and discomfort in FRT (called the mixed bimodal bathtub model). Initially, we investigate the properties of dynamic user equilibrium without perimeter control. Then, we study the equilibrium patterns during perimeter control with transit priority. Unlike existing works, we find that the number of FRT passengers decreases with time toward the desired arrival time and that FRT may not be used around the peak of rush hour. Furthermore, transit priority may not be sufficient to promote the use of FRT, and additional incentive such as subsidy for lower fares may be required to encourage FRT use during perimeter control. Finally, we show that operating many FRT vehicles does not always decrease the equilibrium cost, even under perimeter control with transit priority.

Daily Commuting and Parking Pattern under the Fully Station-Based One-Way Carsharing Vehicle Environment

This study is the first in the literature to investigate the joint equilibrium of departure time and parking location choices in the station-based one-way carsharing system. Time-varying road tolls are optimized to eliminate queueing deadweight loss, minimize schedule delay cost, and induce an economically efficient parking pattern. The round-trip commuting pattern under the fully privately owned vehicle environment was also studied, and the aggregate social cost of the two travel modes are compared with each other to verify the potential benefit gained from implementing the one-way carsharing service. We then investigated demand elasticity with respect to the user’s daily travel cost in a one-way carsharing-only system, and an externality-based charging mechanism is proposed to achieve the optimal demand-supply level. In the one-way carsharing-only travel mode, the following results were obtained: (1) the evening commute pattern is not a simple mirror image of the morning one when spatial parking is considered in both scenarios with and without pricing; (2) when taking account of elastic demand, in optimal time-varying pricing regimes, the charging scheme automatically leads to the socially optimal demand level, but in free-pricing regime, additional user anonymous tax must be charged to achieve the optimal demand level; and (3) the station-based one-way carsharing service benefits the public in aggregate social cost saving compared with the mode of driving a self-owned car.