Urban Rail Transit Congestion Management: Coordinated Optimization of Passenger Guidance and Train Scheduling with Skip-Stop Patterns

Metro stations in metropolitan areas often face the issue of overcrowding, resulting in passengers enduring uncomfortable waiting experiences. Although numerous ongoing studies focus on enhancing transportation efficiency, they tend to overlook the crucial aspects of passenger comfort and experience. Many resources on metro lines can be used to improve passenger comfort, such as unused transportation capacity and platform space. To tackle this pressing concern, our research introduces an innovative multiobjective collaborative optimization model with a primary focus on comfort, efficiency, and cost. The goal is to develop a train timetable that takes skip-stop patterns into account, along with comfort-oriented passenger guidance strategies, and propose a methodology for assessing passenger comfort. We have developed an adaptive large neighborhood search (ALNS) algorithm. By combining it with the Nondominated Sorting Genetic Algorithm II (NSGA-II), we efficiently identify high-caliber solutions. The efficacy of the algorithm and model was corroborated through real-world case studies.