A multi-objective subway timetable optimization approach with minimum passenger time and energy consumption

Summary Considering the service quality and energy efficiency, this paper develops a multi-objective timetable optimization approach for subway system. First, we analyze the variation on the passenger flow at stations, and propose the concept of passenger waiting time. Second, we develop a speed-profile-generation approach to search for the energy-efficient speed profile under the condition of a given section trip time. Then we formulate a multi-objective timetable optimization model to minimize the passenger time and energy consumption by controlling section trip time and station dwell time, in which passenger time includes both waiting time and traveling time. We respectively employ the ideal-point compromise approach, linearly weighted compromise approach and fuzzy linear programming approach to find the suboptimal solution, via performing a genetic algorithm. With the operation data from Beijing Yizhuang and 4-Daxing subway lines of China, we conduct extensive case studies to demonstrate the effectiveness of our model. The results show that the passenger waiting time and energy consumption can be reduced during both peak and off-peak hours. The proposed model and algorithm can be developed to a decision support system for dispatchers to schedule trains in the real world.