Passenger Advice and Rolling Stock Rescheduling Under Uncertainty for Disruption Management

Operators and passengers need to adjust their plans in cases of large-scale disruptions in railway networks. Where most previous research has focused on the operators, this paper studies the combined support of both in a system where passengers have free route choice. In cases of disruption, passengers receive route advice, which they are not required to follow: passengers' route choice depends on the route advice and the timetable information available to them. Simultaneous to providing advice, rolling stock is rescheduled to accommodate the anticipated passenger demand. The duration of the disruption is uncertain, and passenger flows arise from a complex interaction between the passengers' route choices and the seat capacity allocated to the trains. We present an optimization-based algorithm that aims to minimize passenger inconvenience through provision of route advice and rolling stock rescheduling, where the advice optimization and rolling stock rescheduling modules are supported by a passenger simulation model. The algorithm aims to include and evaluate solutions under realistic passenger behavior assumptions. Our computational tests on realistic instances of Netherlands Railways indicate that the addition of the travel advice effectively improves the service quality to the passengers more than only rescheduling rolling stock, even when not all passengers follow the advice.