Last Train Delay Management in Urban Rail Transit Network: Bi-objective MIP Model and Genetic Algorithm

Abstract

Train delay is an inevitable phenomenon in urban rail transit system, but it usually has ripple effects by propagating to other trains and lines. In particular, last train delay has serious effect on transfer passengers’ regular trips. Transfer passengers from last trains concretely fall into two types in this paper: passengers transferring from the last train of feeder line to the last train of connecting line (LtoL) or from the last train of feeder line to un-last train of connecting line (LtoU). Then the last train network delay management model is presented with two objectives: maximizing connecting passengers and minimizing Average Transfer Waiting Time (ATWT) of LtoL transfer passengers. To solve large-scale practical problems rapidly, an efficient genetic algorithm is designed based on this model. Finally, the Beijing subway network is taken as a case study to verify the effectiveness of this model. Under various last train delay scenarios, the results show that the bi-objective model can increase the number of connections between last trains and connecting passengers, meanwhile, there is a big decline in the ATWT of LtoL transfer passengers.