Sensitivity Analysis and Coupled Decisions in Passenger Flow-Based Train Dispatching

Abstract

Frequent train delays make passenger-oriented train dispatching a task of high practical relevance. In case of delays, dispatchers have to decide whether trains should wait for one or several delayed feeder trains or should depart on time. To support dispatchers, we have recently introduced the train dispatching framework PANDA (CASPT 2015).

In this paper, we present and evaluate two enhancements which are also of general interest. 
First, we study the sensitivity of waiting decisions with respect to the accuracy of passenger flow data. More specifically, we develop an integer linear programming formulation for the following optimization problem: Given a critical transfer, what is the minimum number of passengers we have to add or to subtract from the given passenger flow such that the decision would change from waiting to non-waiting or vice versa? 
Based on experiments with realistic passenger flows and delay data from 2015 in Germany, an important empirical finding is that a significant fraction of all decisions is highly sensitive to small changes in passenger flow composition. Hence, very accurate passenger flows are needed in these cases.  

Second, we investigate the practical value of more sophisticated simulations. A simple strategy evaluates the effect of a waiting decision of some critical transfer on passenger delay subject to the assumption that all subsequent decisions are taken according to standard waiting time rules, as usually employed by railway companies like Deutsche Bahn. Here we analyze the impact of a higher level of simulation where waiting decisions for a critical transfer are considered jointly with one or more other decisions for subsequent transfers. We learn that such "coupled decisions" lead to improved solution in about 6.3% of all considered cases.