Effects of stochasticity on recovery solutions in the case of high-density rail/metro networks

This paper proposes an off-line procedure for the management of rail-metro networks in the case of high-density, congested cities. Our approach maybe considered a decision support system for establishing recovery plans which take customers’ needs into accountwhen service disruption occurs. This new methodology combines a stochastic microscopic simulation of the network and passenger flows so as to evaluate effects of implemented strategies on service quality, simulating rail operations as closely as possible to reality. In other words, in order to analyse the robustness and feasibility of allowable recovery action, the method in questionconsiders the randomness of breakdown occurrence and the variability of both dwell times and travel demand levels at stations. Therefore, user flows are assigned to the network, simulating passenger behaviour on the platforms and especially the dynamic interaction with the rail system. The latter results in a fixed-point problem for the estimation of average dwell times. This, especially in high-density contexts, represents one of the main disturbances of the service and cannot be neglected. Based on this analysis, the stochastic distribution function is evaluated. Finally, an application on a real metro line in the south of Italy is performed to show the effectiveness of the proposed approach.