Effective train routing selection for real-time traffic management: Improved model and ACO parallel computing

Abstract

The real-time Railway Traffic Management Problem (rtRTMP) is the problem of detecting and solving time-overlapping conflicting requests made by multiple trains on the same track resources. This problem consists in retiming, reordering and rerouting trains in such a way that the propagation of disturbances in the railway network is minimized. The rtRTMP is an NP-complete problem and finding good strategies to simplify its solution process is paramount to obtain good quality results in a short computation time. Solving the Train Routing Selection Problem (TRSP) aims to reduce the size of rtRTMP instances by limiting the number of routing variables: during the pre-processing, the most promising routing alternatives among the available ones are selected for each train. Then, the selected alternatives are the only ones used for the rtRTMP. A first version of the TRSP has been recently proposed in the literature. This paper presents an improved TRSP model, where rolling stock re-utilization timing constraints and estimation of train delay propagation are taken into account. Additionally, a parallel Ant Colony Optimization (ACO) algorithm is proposed. We analyze the impact of the TRSP model and algorithm on the rtRTMP through a thorough computational campaign performed on a French case study with timetable disturbances and infrastructure disruptions. The presented model leads to a better correlation between TRSP and rtRTMP solutions, and the proposed ACO algorithm outperforms the state-of-the-art algorithm.