A dynamic schedule-based model for congested transit networks

In this paper we propose a model and algorithm for solving the equilibrium assignment problem in a congested, dynamic and schedule-based transit network. We assume that the time varying origin-destination trip demand is given. All travelers have full predictive information (that have been gained through past experience) about present and future network conditions and select paths that minimize a generalized cost function that encompasses four components: (a) in-vehicle time; (b) waiting time; (c) walking time; and (d) a line change penalty. All transit vehicles have a fixed capacity and operate precisely as specified in preset timetables. Passengers queue at platforms according to the single channel first-in-first-out discipline. By using time-increment simulation, the passenger demand is loaded onto the network and the available capacity of each vehicle is updated dynamically. After each simulation run, the passenger arrival and departure profiles at all stations are recorded and these are used to predict dynamic queuing delays. From such delays, minimum paths are updated and used for the next simulation run. The user equilibrium assignment problem is solved iteratively by the method of successive averages. (C) 2003 Elsevier Science Ltd. All rights reserved.