Optimizing the Urban Rail Transit System for Minimal Passenger Waiting Time and Reduced Traction Energy Consumption

Urban rail transit systems face challenges due to increased energy costs and the growing need for cleaner energy solutions. The task of reducing energy consumption while ensuring reliable, safe, and timely transportation for passengers is a major challenge. This study addresses the problem by developing an optimization model to fine-tune the train timetables by adjusting stop times. Factors considered within the model included passenger demand, scheduling limitations, train intervals, capacity, and stop times. To reduce traction energy consumption, this paper suggests extending the running time of the train’s energy-efficient driving strategy by optimizing stop times based on fixed timetables. The primary goal is to minimize passenger waiting times and traction energy consumption. Therefore, a specialized genetic algorithm specifically designed to align with the characteristics of the optimization model was employed to solve the optimization problem effectively. The validation of the proposed model and algorithm was demonstrated through a case study involving a one-way subway line with varying running intervals. Comparative analysis with the results of the fixed stop time scenario, the research found that through the optimization of the stop time, there was an improvement of 3.94% in passenger waiting time, indicating a substantial increase in efficiency. Additionally, there was a 5% reduction in traction energy consumption. These findings highlight significant steps in improving the passenger experience while concurrently reducing the energy consumption of trains in urban rail transit.