Railway traffic is often perturbed by unexpected events causing delays, which may greatly propagate. Nowadays, dispatchers deal with delays trying to limit this propagation with scarce decision support tools. RECIFE-MILP is an optimization algorithm which may be used to support dispatchers' decisions. In this paper, the authors illustrate the analysis performed in collaboration with the French infrastructure manager (SNCF Reseau) to assess the actual impact of the application of optimization in real-time railway traffic management. The authors perform a twofold experimental analysis on two French complex junctions characterized by intense mixed (passenger and freight) traffic. On the one hand, they assess this impact on scenarios specifically identified as relevant by SNCF Reseau. On the other hand, they tackle actually occurred scenarios and they compare the decisions made by RECIFE-MILP with those made by the dispatchers who actually faced the perturbation. These experiments show through simulation that the optimization may remarkably improve the way traffic is managed.
['Paola Pellegrini', 'Grégory Marliere', 'Joaquin Rodriguez']
A detailed analysis of the actual impact of real-time railway traffic management optimization