Stochastic User Equilibrium Model with Implicit Travel Time Budget Constraint

Abstract

Although time constraints on travel behavior have been widely recognized, little effort has been made to incorporate such constraints into the traditional stochastic user equilibrium (SUE) framework. The major objective of this research is to fill this gap by incorporating travel time constraints into the SUE model by means of a nonlinear perceived travel time function. This modified model, designated the travel time budget model, focuses primarily on discretionary travel behavior (such as shopping trips) and hence also allows the possibility of deferring travel decisions by incorporating an additional choice alternative designated the shop-less-frequently alternative. This model is compared with the traditional SUE model by using a simulated travel scenario on a test network designed to reflect a practical planning situation. The simulation shows that when attractiveness levels are increased by the introduction of a new shopping opportunity, the presence of travel time constraints can lead to significantly smaller predicted travel volumes than those of the traditional SUE model. More important, it shows that the overall pattern of travel can be quite different. In particular, travel to the shopping destination with enhanced attractiveness can actually decrease for some origin locations. The findings suggest that when an attempt is made to evaluate the impact of planning alternatives on future traffic patterns, it is vital to consider not only the cost of time itself but also the time trade-offs between travel and other human activities.

TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

“…Understanding the commuter flow in a transportation system is an important research topic. In the existing literature, many of the works focus on studying commuter flow models based on experience [6,7,12]. The models depend heavily on behavior assumptions and hence lack reliable empirical data verification.…”

Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable

“…Conventionally, models to solve passenger flow assignment problems can be classified according to whether Wardrop's principle is followed [5]. One model is the nonequilibrium assignment, and the other is the equilibrium assignment model [6]. Moreover, it is assumed that the process of passengers' choice has some random characteristics because of imperfect knowledge of travel time, individual differences, measurement errors, and so on [5][6][7].…”

“…One model is the nonequilibrium assignment, and the other is the equilibrium assignment model [6]. Moreover, it is assumed that the process of passengers' choice has some random characteristics because of imperfect knowledge of travel time, individual differences, measurement errors, and so on [5][6][7]. Therefore, confronted with today's metro systems, the result from passengers' route choices can be described more appropriately by the stochastic user-equilibrium (SUE) with time and space constraints, which is also proved by some simulation experiments [5,8] and full-scale case tests [4].…”