Li, Haodong (Beijing Jiaotong University), Yin, Jiateng (Beijing Jiaotong University), TANG, Tao (Beijing Jiaotong University), D'Ariano, Andrea (Università degli Studi Roma Tre), You, Mengxing (Nanchang Rail Transit Group Co., Ltd.)

Integrated Optimization of Energy-Efficient Timetable and Speed Profiles for Train Platoons in Urban Rail Transit Systems

Scheduled for presentation during the Regular Session "Rail Traffic Management II" (FrBT7), Friday, September 27, 2024, 14:50−15:10, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Train platooning, which allows multiple train units to be virtually coupled into a platoon with very short following distances, has become an emerging technology in railway industry. Our study investigates the energy-efficient operation of train platoons to reduce the energy consumption of trains while satisfying time-varying passenger demands in practice. Specifically, we construct a mixed integer programming (MIP) model in order to simultaneously optimize the timetable and speed profiles of train platoons in an urban rail transit line. The aim of the developed MIP model is to minimize the total energy consumption of trains as well as the waiting time of traveling passengers, while satisfying the practical constraints, such as the train movement constraints, passenger loading constraints for different train platoons, etc. In order to solve the MIP model more efficiently, we develop a hybrid iterative algorithm that combines a Deep Q-Network (DQN) and an MIP solver, in which the DQN generates energy-efficient speed profiles for different types of platoons and the MIP solver then determines the optimal train schedule. Finally, we conduct real-world case studies on the Beijing subway Yizhuang line to verify the effectiveness of our approach. The results indicate that the energy-efficient operation of train platoons can achieve a better trade-off between service quality and system energy consumption.