Citation

Tessitore ML, Samà M, D'Ariano A, Hélouet L, Pacciarelli D. Transp. Res. C Emerg. Technol. 2022; 136: e103525.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trc.2021.103525

PMID

unavailable

Abstract

This paper analyses how train delays propagate in a metro network due to disturbances and disruptions when different recovery strategies are implemented. Metro regulators use traffic management policies to recover from delays as fast as possible, return to a predefined schedule, or achieve an expected regularity of train arrivals and departures. We use as a metro traffic simulator SIMSTORS, which is based on a Stochastic Petri Net variant and simulates a physical system controlled by traffic management algorithms. To model existing metro networks, SIMSTORS has been mainly used with rule-based traffic management algorithms. In this work, we enhance traffic management strategies. We integrate SIMSTORS and the AGLIBRARY optimization solver in a closed-loop framework. AGLIBRARY is a deterministic solver for managing complex scheduling and routing problems. We formulate the real-time train rescheduling problem by means of alternative graphs, and use the decision procedures of AGLIBRARY to obtain rescheduling solutions. Several operational issues have been investigated throughout the use of the proposed simulation-optimization framework, among which how to design suitable periodic or event-based rescheduling strategies, how to setup the traffic prediction horizon, how to decide the frequency and the length of the optimization process. The Santiago Metro Line 1, in Chile, is used as a practical case study. Experiments with this framework in various settings show that integrating the optimization algorithms provided by AGLIBRARY to the rule-based traffic management embedded in SIMSTORS optimizes performance of the network, both in terms of train delay minimization and service regularity.

Language: en

Keywords

Advanced public transport systems; Alternative graph; Closed-loop framework; Heuristic algorithms; Train scheduling