Neural Network-Based Subway Regenerative Energy Optimization With Variable Headway Constraints

Abstract

For subway regenerative energy optimization, the essential idea is to maximize the amount of regenerative energy absorption (AREA) between accelerating trains and braking trains by adjusting train timetable. The bottlenecks preventing the theoretical research from being used to industrial practice lie in the time-consuming simulation of AREA and the constant setting of headway threshold, which seriously reduces the computational efficiency and compresses the solution space. For addressing these issues, we formulate neural networks to speed up the AREA simulation, develop a bisection algorithm to dynamically adjust the headway threshold under moving block mechanism, and then establish a hybrid heuristic method integrating neural networks, genetic algorithm, variable neighborhood search and simulated annealing to determine the timetable, including dwell time distribution (DTD) at stations and headway time distribution (HTD) among consecutive trains. The effectiveness of the proposed method is confirmed by numerical experiments based on the real ATO data of Beijing Subway Changping Line. The results reveal that the neural networks could save 98.96% of the computational time at the expense of 1.76% of the accuracy loss, such that the hybrid heuristic algorithm could save 75.68% of the computational time. Benchmarked with the constant headway constraints used in literature, the variable headway constraints could achieve an average improvement of AREA by 8.94%. In addition, the joint optimization of DTD and HTD could increase the AREA by 15.28% on average, compared to the single optimization of HTD. This research is of great significance to improve the practicability of subway regenerative energy optimization algorithms.