Departure Time Choice Behavior Research Articles

A Departure Time Choice for Morning Commute Considering Train Capacity of a Rail Transit Line

Every commuter utilizing urban rail transit decides the departure time from home to a station according to individual judgment for the biggest possibility to board a train as soon as possible after the arrival. Therefore, the departure time choice behavior of the commuters is complicated especially when the transport capacity of this transit line cannot meet the travel demands of its users in rush hour. This research first develops a travel cost function mainly considering the travel time to rationally describe the volume distribution of the commuters arriving at a station at different time periods. Furthermore, an optimization model is accordingly proposed to rationalize the arrival distribution of the commuters on the basis of the amount of the arrived commuters who are able to board a train from the perspective of the systematic operation of a rail transit line for the minimal total travel cost of all the commuters along this line. It is found that the total travel cost can be reduced by 19.0% at most through the arrival distribution rationalization with the optimization model.

How the Risk-Taking Personality Influences Commute Drivers’ Departure Time Choices

This study is an empirical analysis of departure time choice behaviors of commute drivers on the basis of three month (Jul.∼Sep., 2006) data from the Electronic Toll Collection (ETC) systems in Tokyo, Japan. Assuming drivers’ risk taking personalities can be represented by their different driving behaviors, through proposing the usage of Travel Speed Standard Scores (TSSS), we firstly categorize the drivers into three groups: Fast Drivers who show a personality to take risk, Slow Drivers who show a personality to avoid risk, and Normal Drivers who are between the Fast and Slow Drivers. A comparison study is then carried on, which shows that the departure time choice behaviors between the Fast and the Slow drivers are significant different from each other. The former tends to depart later and with a smaller deviation. This paper aims helping to clarify the influence of risk taking personalities on transportation choices. © 2011 Published by Elsevier Ltd.

The impacts of road pricing on route and mode choice behaviour

This paper summarises the results of an extensive stated preference (SP) survey, conducted in Switzerland in order to obtain detailed information for the evaluation of road pricing schemes. Four different SP experiments were included: one about the political acceptability of road pricing and the other three about route, mode and departure time choice behaviour in the presence of road pricing. The questionnaires of 1005 respondents were used in a multinomial logit analysis of the latter three experiments. The values of travel time savings (VTTS) depended nonlinearly on the individual's income, travel time and overall travel costs. The respondents' evaluation of the cost components of fuel, tolls and parking differed significantly. Shifts in departure time depended on the choice situation were negatively valued. Furthermore, the importance of political preferences, as measured in the SP survey on acceptability, could be demonstrated, as the inclusion improved the model fit significantly as well as the reliability of the results.

Assessing the benefits of integrated en-route transit information systems and time-varying transit pricing systems in a congested transit network

This paper proposes a model for assessing the effects of the integrated implementation of en-route transit information systems (ETIS) and time-varying transit pricing systems (TTPS). The proposed model reveals the interaction between ETIS and TTPS, and the potential benefit of the joint implementation. There are two classes of passengers: those equipped and those unequipped with ETIS. It is assumed that unequipped passengers make their travel choices according to stochastic dynamic user optimal principles, with equipped passengers having a lower perception variation of travel cost due to the availability of better information. A bi-level program is formulated to model the integrated effects on passengers’ departure time choice behavior, route choice behavior, transit network performance, and transit operators’ revenue. The lower level is a multi-class stochastic dynamic transit assignment model. The combined system total cost and operators’ benefits under varied transit conditions are investigated with a numerical example.

Modeling Departure Time Choice under Stochastic Networks Involved in Network Design

Different travel cost functions for modeling travelers’ departure time and route choice behavior under uncertainty are investigated; then the principal equivalency of the mean-variance and scheduling approaches in modeling departure time choice is proven analytically. Based on the equivalency, a general cost function is formulated for modeling travelers’ departure time and route choice behavior under uncertainty and applied to an analysis of the departure patterns under uncertainty and a reliability-based dynamic network design problem. The analytical investigation of the departure pattern considering travel time reliability in the individual's travel cost function is presented as an extension of Vickrey's deterministic bottleneck model toward stochastic capacities. The departure pattern is significantly different from that without reliability: travelers depart earlier to reduce travel time variability. This finding (corroborated by simulations) motivated the network design approach including travel time reliability in departure time and route choice behavior. A discrete dynamic network design problem is formulated subject to a reliability-based dynamic user equilibrium, with the cost function derived from the equivalency analysis among different cost functions. Total network costs–the costs of total travel (including travel time reliability) and construction–are minimized. The network design problem consists of optimally allocating link capacities by determining the optimal numbers of lanes for a subset or a full set of links of a potential network. A genetic algorithm with some intelligence is adopted to solve the discrete optimization problem. Use of a small hypothetical network indicates that different network designs may lead to diverse departure distributions among different origin-destination (O-D) pairs over time, directly affecting travel costs. Relatively dissimilar departure patterns among different O-D pairs over time give lower travel costs.

6週間の交通日誌データを用いた交通行動の変動特性の分析－活動発生と出発時刻を例に

This paper attempts to examine the variation properties of activity generation and departure time choice behavior by activity type, using a continuous six-week travel survey data collected in the cities of Karlsruhe and Halle in Germany in 1999. The total variation is decomposed into intra-individual variation, inter-individual variation, inter-household variation, systematic day-to-day variation, and spatial variation. It is confirmed that different activities and different behavioral aspects show quite different variations, and, among the variations, the intra-individual variation accounts for the largest percentage of the total variation.

Exploring Variation Properties of Departure Time Choice Behavior by Using Multilevel Analysis Approach

The variation properties of departure time choice behavior are examined by activity type with the use of a continuous 6-week travel survey collected in the cities of Karlsruhe and Halle, Germany, in 1999. The total variation in departure time choice is divided into five variation components: spatial variation, temporal variation at the aggregate level, interhousehold variation, interindividual variation, and intraindividual variation. These variations are first quantitatively analyzed by using a multilevel modeling approach without considering the influences of explanatory variables. Then, on the basis of the clarified variations patterns, how much of the variations can be captured by explanatory variables is examined. It is confirmed that different activities show quite different variations, and of the variations the intraindividual variation accounts for the largest percentage of the total variation. Model estimation results further underscore the need for simultaneously dealing with unobserved macrolevel variations, especially interindividual, interhousehold, and spatial variations, as well as the intraindividual variation, even when the explanatory variables are included in the model. Such quantitative assessment of various types of variations based on a multilevel modeling approach provides a useful guide for the specification of behavior models and deepens the knowledge of behavioral variability by going beyond the traditional paradigm, which focuses on a limited set of variations.

BRAESS'S PARADOXES IN DYNAMIC TRAFFIC ASSIGNMENT WITH SIMULTANEOUS DEPARTURE TIME AND ROUTE CHOICES

In this paper, we investigate some new dynamic phenomena of Braess's paradox in transportation networks when both the departure time and route choices are considered simultaneously. The classical Wardropian user equilibrium principle is used to characterize route choice behavior, and the traditional bottleneck models with deterministic queues are employed to describe user's departure time choice behavior. New Braess's paradoxes with dynamic user response have been found in respect to the point queue and physical queue assumptions, and the mechanisms of such paradoxes are analyzed in details. Furthermore, it is shown in a numerical example that a paradox may occur in network when physical queue assumption is made, but the paradox may not be found in network if the point queue assumption is adopted in the proposed dynamic traffic assignment model. Due to the significant difference between point queue and physical queue assumptions, spatial dimension of queues has to be considered carefully in dynamic traffic assignment models. Finally, some traffic management measures are suggested and examined to resolve the potential paradoxes, such as link closing, ramp metering, dynamic road pricing, lane partitioning, and adaptive traffic control etc.

Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.

Drivers' Mental Representation of Travel Time and Departure Time Choice in Uncertain Traffic Network Conditions

The decisions drivers make, such as choice of route or departure time, constitute typical decision making under uncertainty. Drivers' decision making has been studied within the framework of expected utility theory. However, empirical decisional phenomena violating the premise of expected utility theory have been observed repeatedly. These findings have indicated that decision making is critically affected by the decision frame. It has also been pointed out that the uncertainty of outcome is perceived as an interval of possible resultant values. Based on these findings, we propose hypotheses that: (1) a driver perceives an uncertain travel time as an interval, and (2) a driver decides on a departure time based on a decision frame edited by this interval. To test these hypotheses, we collected data on drivers' departure time choice behavior, n = 335. Decisional phenomena found in this study confirm our hypotheses.

Using Global Positioning System Data to Understand Day-to-Day Dynamics of Morning Commute Behavior

The day-to-day variability of journey-to-work trips, including departure time, route choice, and trip-chaining behavior, was examined with Global Positioning System-based disaggregate morning commute data for 56 drivers during a 1-week period. Data were collected from the ongoing instrumented vehicle projects in Atlanta sponsored by NHTSA and FHWA. The study examines alternative measurements of the day-to-day variability of the commute pattern. While commuting trips are often thought to be highly repetitious and therefore highly predictable trips, research results indicate that commuters change departure times more frequently than routes, and trip chaining significantly affects commuters' departure time and route choice behavior. This study begins to explore definitions and relationships that will be necessary to better understand the day-to-day commute dynamics.

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.

Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior

Stochastic learning automata (SLA) theory is used to model the learning behavior of commuters within the context of the combined departure time route choice (CDTRC) problem. The SLA model uses a reinforcement scheme to model the learning behavior of drivers. A multiaction linear reward-ϵ-penalty reinforcement scheme was introduced to model the learning behavior of travelers based on past departure time choice and route choice. A traffic simulation was developed to test the model. The results of the simulation are intended to show that drivers learn the best CDTRC option, and the network achieves user equilibrium in the long run. Results indicate that the developed SLA model accurately portrays the learning behavior of drivers, while the network satisfies user equilibrium conditions.

Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing

Driver behavior under congestion pricing is analyzed to evaluate the effects of alternative congestion pricing schemes. The analysis, which is based on stated preference survey results, focuses on time allocation, departure time choice, and route choice when a congestion pricing scheme is implemented on toll roads in Japan. A unique feature of the model system of this study is that departure time choice and route choice are analyzed in conjunction with the activities before and after the trip. A time allocation model is developed to describe departure time choice, and a route and departure time choice model is developed as a multinomial logit model with alternatives representing the choice between freeways and surface streets and, for departure time, the choice from among before, during, or after the period when congestion pricing is in effect. The results of the empirical analysis suggest that departing during the congestion pricing period tends to have higher utilities and that a worker and a nonworker have quite different utility functions. The comparative analysis of different congestion pricing schemes is carried out based on the estimated parameters. The results suggest that the probability of choosing each alternative is stable even if the length of the congestion pricing period changes, but a higher congestion price causes more drivers to change the departure time to before the congestion pricing period.

METROPOLIS: Modular System for Dynamic Traffic Simulation

METROPOLIS proposes an interactive environment that simulates automobile traffic in large urban areas. The core of the system is a dynamic simulator that integrates commuters’ departure time and route choice behaviors over large networks: Drivers are assumed to minimize a generalized travel cost function that depends on travel time and schedule delay. This simulator is based on a behavioral driver information process. It allows real-time and off-line simulations. The system also includes a scenario builder and a graphical results viewer. The main ideas underlying METROPOLIS are presented, and preliminary computer simulation experiments are discussed for Geneva, Switzerland.