A Real-Time Railway Traffic Management Approach Preserving Passenger Connections

Abstract

This paper addresses the real-time Railway Traffic Management Problem (rtRTMP), which involves adjusting train timetables during perturbations. Perturbations in railway networks often lead to significant delays, necessitating strategies to minimize their propagation. An important objective of traffic management is to facilitate passenger transfers through connecting trains, which may become difficult when traffic is disturbed. Pursuing this objective, the paper focuses on mitigating train delays by reducing connection times during transfers without compromising connections. To achieve this, we extend an existing Mixed-Integer Linear Programming (MILP) formulation for the rtRTMP by introducing two alternative enhancements. Moreover, we pursue the same delay mitigation by extending an Ant Colony Optimization algorithm for the Train Routing Selection Problem (TRSP): this problem reduces the number of alternative routes to be considered for trains, making rtRTMP instances tractable. We assess the efficiency of the proposed enhancements in reducing the total train delay while preserving passenger connections in multiple instances representing traffic in the Lille-Flandres station control area, located in the north of France. The results demonstrate that the integration of these enhancements, in both the TRSP and the rtRTMP, results in a significant reduction in delay propagation.

Keywords

• Ant colony optimization
• connection time
• passenger connections
• real-time railway traffic management problem (rtRTMP)
• rescheduling
• train rerouting