Departure Time Changes Research Articles

A micro-simulation model system of departure time using a perception updating model under travel time uncertainty

Existing microscopic traffic models have often neglected departure time change as a possible response to congestion. In addition, they lack a formal model of how travellers base their daily travel decisions on the accumulated experience gathered from repetitively travelling through the transport network. This paper proposes an approach to account for these shortcomings. A micro-simulation approach is applied, in which individuals base their consecutive departure time decisions on a mental model. The mental model is the outcome of a continuous process of perception updating according to principles of reinforcement learning. Individuals’ daily travel decisions are linked to the traffic simulator SIAS-PARAMICS to create a simulation system in which both individual decision-making and system performance (and interactions between these two levels) are adequately represented. The model is applied in a case study that supports the feasibility of this approach.

Analyzing work departure time variability in Brussels

– This study explores the dynamics of departure time for the afternoon commute, which has received little attention both in theoretical and empirical work. Reported monthly and weekly departure time data, obtained from a survey of Brussels commuters, are used to study the impact of socio-economic, transportation and workrelated variables on the propensity of afternoon departure time changes. Binary and ordered probit models are used to estimate the impacts of these factors on two measures of departure time change propensity. The first one is the reported frequency of departure time changes in a month and the second measure is the time (in minutes) that a respondent left earlier or later than their normal time during the last five working days. In the first measure, the respondent selects his or her own threshold for reporting departure time change, whereas in the second measure the respondent merely provides the deviation from his or her normal departure time. The two measures together suggest three types of respondent groups: A group that perceives a wide-window (greater than or equal to 30 minutes) of regular departure times as normal, a group that perceives a narrow window of departure time as normal and change departure time occasionally and a third group that does not change their normal departure time. Statistical evidence indicates that work-related factors, particularly tolerant policy of the employer toward leaving work earlier, flextime and occupation type (scientific and executive professions) are associated with higher propensity of departure time changes. The implications of the findings are discussed.

A Multi-Class Dynamic User Equilibrium Model for Queuing Networks with Advanced Traveler Information Systems

This paper presents a formulation and solution algorithm for a composite dynamic user-equilibrium assignment problem with multi-user classes, in order to assess the impacts of Advanced Traveler Information Systems (ATIS) in general networks with queues. Suppose that users equipped with ATIS will receive complete information and hence be able to choose the best departure times and routes in a deterministic manner, while users not equipped with ATIS will have incomplete information and hence may make decisions on departure times and routes in a stochastic manner. This paper proposes a discrete-time, finite-dimensional variational inequality formulation that involves two criteria regarding the route and departure time choice behaviors, i.e., the deterministic dynamic user equilibrium and the nested logit-based stochastic dynamic user equilibrium. The formulation is then converted to an equivalent “zero-extreme value” minimization problem. A heuristic algorithm based on route/time-swapping process is proposed, which iteratively adjusts the route and departure time choices to reach closely to an extreme point of the minimization problem. A numerical example is used to demonstrate the effectiveness of the proposed approach for assessing the ATIS impacts such as changes in individual travel costs, departure times, route inflows, queuing peaks and total network travel cost.

Bus Timetables with Even Passenger Loads as Opposed to Even Headways

Bus timetables with even headways often require passengers to adjust to given timetables instead of adjusting the timetables to fluctuating passenger demand. This expectation is a main source of unreliable service that extends passenger waiting times at bus stops and creates overcrowding. Alternative transit timetables are created to improve the correspondence of vehicle departure times with passenger demand while minimizing resources (number of buses required). An automated method is presented. With the growing problems of transit reliability, and the advance in the technology of passenger information systems, even and clock headways become less important. Therefore, more efficient schedules from both the passenger and operator perspectives can be realized. The method presented uses an informative graphic technique. This approach enables the scheduler to examine changes in bus departure times and to obtain immediate feedback on the expected average passenger loads.

Commuter Responses to Travel Time Uncertainty under Congested Conditions: Expected Costs and the Provision of Information

A simulation methodology for analyzing policies for reducing traffic congestion and the effect of providing commuters with pre-trip information is developed. This combines a model incorporating travel time uncertainty to determine optimal home departure times with a supply-side congestion model of a highway facility. Congestion is endogenously generated for different scenarios using a randomly generated normal distribution of work start times and previously estimated parameters. The results suggest that greater reductions in the expected costs of commuting may be achievable by policies that reduce travel time variance rather than just travel times. Policy simulations which analyze the congestion effects of providing commuters with pre-trip information are found to not necessarily provide many benefits to those using the information, although gains may be minor because the only behavioral response is limited to departure time changes.

The impact of adverse weather conditions on the propensity to change travel decisions: A survey of Brussels commuters

New developments in transportation systems and the increasing complexities of urban living are creating challenges and opportunities to comprehensively address transportation problems. The key to exploiting new opportunities lies in understanding how travelers would respond to changes in the transportation system. This study aims to understand traveler behavior under normal and unexpected travel conditions in real-life situations. The results of a comprehensive behavioral survey conducted in Brussels are reported. Commuters' mode, departure time and route selection decisions, and the factors that influence them, including travel conditions, personal and household characteristics, and situational constraints, are explored. The paper develops a deeper understanding of automobile commuters' response to adverse weather. Automobile commuters constitute more than 50% of the total sample ( N = 1218). They were asked about adverse weather impacts on their mode, route and departure time changes. Among those who changed their travel decisions in adverse weather (about 50%), more than one-quarter reported that adverse weather was either very important or important in changing their mode; and 60% changed their departure time due to adverse weather whereas 35% diverted to alternate routes. Furthermore, close to 75% kept themselves informed about weather through secondary information sources such as radio and television. To analyze relationships empirically, ordered probit models of mode and departure time were estimated. The models show that commuters change their travel patterns in systematic ways when adverse weather occurs. Finally, the implications of the survey results for dealing with adverse weather problems are discussed.

Commuters’ Departure Time Decisions in Brussels, Belgium

Factors that influence commuters’ departure time decisions are explored, especially the trade-off between travel time and schedule delay. Stated and reported behavior data obtained from a survey of commuters in Brussels, Belgium, were used to analyze the influence of socioeconomic and contextual variables. The key findings were as follows. Daily schedules for flextime and fixed-time commuters were quite similar, suggesting that flextime commuters do not extensively use their flexibility to avoid peak-period congestion. When commuters changed their departure times between home and work, their arrival times shifted by a similar amount. This implies that the shortening of travel time is not as critical as other reasons, such as requirements and personal convenience, in motivating departure time changes. Furthermore, 35 to 50 percent of the respondents were unwilling to change their departure times to save 10 min of travel time. Therefore, departure time changes may not be feasible in many cases for the range of travel times encountered in urban areas. Among those willing to make further trade-offs by changing departure times, the values for the early and late schedule delay–travel time trade-off were similar for both the stated and the reported preferences and were broadly consistent with those from other studies. The travel time–schedule delay trade-off values are calculated for the a.m. and p.m. commutes. Commuters who experienced longer travel times were more likely to change their departure times. When changing departure times, females and managers were less likely to depart from home later than usual, and managers were also more likely to depart earlier than usual. To analyze relationships empirically, ordinary-least-squares and tobit models of departure time are estimated. Finally, the implications are discussed.

Effect of traffic information on commuters' propensity to change route and departure time

AbstractConsiderable public and private resources are devoted to the collection and dissemination of real‐time traffic information in the Chicago area. Such information is intended to help individuals make more informed travel decisions, yet its effect on behavior remains largely unexplored. This study evaluates the effect of traffic information on travelers' route and departure time changes and provides a stronger basis for developing advanced information systems.Downtown Chicago automobile commuters were surveyed during the AM peak period. The results indicate that a majority of the respondents access, use and respond to information. For example, individuals use travel information to reduce their anxiety—even if they do not change travel decisions; this indicates that information may have “intrinsic” value. That is, simply knowing traffic conditions is valued by travelers. More than 60% of the respondents had used traffic information to modify their travel decisions. Multivariate analysis using the ordered probit model showed that individuals were more likely to use traffic reports for their route changes if they perceived traffic reports to be accurate and timely, and frequently listened to traffic reports. Respondents were more likely to change their departure times if they perceived traffic reports to be accurate and relevant, and frequently listened to traffic reports.The implication for Advanced Traveler Information Systems are that they may be designed to support both enroute and pre‐trip decisions. ATIS performance, measured in terms of accuracy, relevance and timeliness would be critical in the success of such systems. Further, near‐term prediction of traffic conditions on congested and unreliable routes (where conditions change rapidly) and incident durations is desirable.

Poisson analysis of commuter flexibility in changing routes and departure times

This paper investigates the determinants of commuter flexibility in changing routes and departure times for the morning trip to work. A sample of commuters in a congested metropolitan area is used to estimate Poisson regressions of how often commuters change routes and departure times per month. The estimation results provide valuable insight into the effects of traffic system and socioeconomic conditions on the frequency of route and departure time changes. The results also have significant implications for real-time traffic information systems and future research on the route and departure time choice decision-making process.