Urban rail transit planning using a two-stage simulation-based optimization approach

In urban metro systems, stochastic disturbances occur repeatedly as a result of an increment of demands or travel time variations, therefore, improving the service quality and robustness through minimizing the passengers waiting time is a real challenge. To deal with dwell time variability, travel time and demand uncertainty, a two-stage GA-based simulation optimization approach is proposed in order to minimize the expected passenger waiting times. The proposed method here has the capability of generating robust timetables for a daily operation of a single-loop urban transit rail system. The first stage of the algorithm includes the evaluation of even-headway timetables through simulation experiments. In the second stage, the search space is limited to the uneven-headway patterns in such a manner where the algorithm keeps the average of headways close to the best even-headway timetable, obtained from the first stage. The optimization is intended to adjust headways through simulation experiments. Computational experiments are conducted on Tehran Metropolitan Railway (IRAN) and the outcomes of optimized timetable obtained by this proposed method are demonstrated. This newly proposed two-stage search approach could achieve to a more efficient solution and speed up the algorithm convergence.