On the tactical and operational train routing selection problem. (March 2017)

• Record Type: Journal Article
• Authors: Samà, Marcella; Pellegrini, Paola; D'Ariano, Andrea; Rodriguez, Joaquin; Pacciarelli, Dario
• Is Part Of: Transportation research. Volume 76(2017)
• Page Start: 1
• Page End: 15
• Publication Date: 2017-03
• Subjects: Real-time railway traffic management -- Ant Colony Optimization -- Routing -- Scheduling; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.12.010
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We deal with the problem of selecting a subset of routing alternatives for each train. We investigate when it is the best moment to perform the routing selection. The trade-off between abundant computation time and updated information is tackled. The problem is solved using two variants of an Ant Colony Optimization algorithm. The best approach depends on the predictability of the perturbation considered. Abstract: In the real-time railway traffic management problem, the number of alternative routings available to each train strongly affects the size of the problem and the time required to optimally solve it. The train routing selection problem identifies a suitable subset of alternative routings to be used for each train in the real-time railway traffic management. This paper analyzes the impact of solving the train routing selection problem at different levels. The problem can be solved at tactical level right after the timetabling process, using historical traffic data and with abundant computation time. In this case the problem constitutes an integration step between the timetabling and the real-time traffic management. Alternatively, the problem can be solved at operational level right before the real-time railway traffic management problem solution, using up to date traffic perturbation and a real-time time limit of computation. Experiments are performed on two French test cases, the line around Rouen and the Lille station area, for several disturbed and disrupted scenarios. The results show that the best approach depends on the type of traffic disturbance tackled.