Passenger Flow-Oriented Train Disposition

Abstract

Disposition management solves the decision problem whether a train should wait for incoming delayed trains or not. This problem has a highly dynamic nature due to a steady stream of update information about delayed trains. A dispatcher has to solve a global optimization problem since his decisions have an effect on the whole network, but he takes only local decisions for subnetworks (for few stations and only for departure events in the near future). In this paper, we introduce a new model for an optimization tool. Our implementation includes as building blocks (1) routines for the permanent update of our graph model subject to incoming delay messages, (2) routines for forecasting future arrival and departure times, (3) the update of passenger flows subject to several rerouting strategies (including dynamic shortest path queries), and (4) the simulation of passenger flows. The general objective is the satisfaction of passengers. We propose three different formalizations of objective functions to capture this goal. Experiments on test data with the train schedule of German Railways and real delay messages show that our disposition tool can compute waiting decisions within a few seconds. In a test with artificial passenger flows it is fast enough to handle the typical amount of decisions which have to be taken within a period of 15 minutes in real time.

This work was supported by the DFG Focus Program Algorithm Engineering, grant MU 1482/4-2. We wish to thank Deutsche Bahn AG for providing us timetable data for scientific use.