Optimizing passengers’ boarding and alighting operations in urban mass transit

Abstract

During boarding and alighting in urban mass transit, some train doors become overcrowded while others are underutilized. This imbalance increases dwell time as trains wait for the last passengers to board or alight. We propose a nonconvex MINLP to minimize boarding and alighting times—a function of the number of boarding and alighting passengers—by optimally allocating passengers to doors. To capture the process of passengers’ choosing doors, we developed a choice model with financial incentives to guide passengers to specific doors. Optimal discounts are determined within the choice model and integrated into the optimization framework to adjust passengers per door. ___________________________________________________