Travel-Energy-Based Timetable Optimization in Urban Subway Systems

Jian Li, Lu Zhang, Bu Liu, Ningning Shi, Liang Li and Haodong Yin ()
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Jian Li: Beijing Rail Transit Construction Management, Co., Ltd., Beijing 100068, China
Lu Zhang: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Bu Liu: Beijing Rail Transit Construction Management, Co., Ltd., Beijing 100068, China
Ningning Shi: Beijing Rail Transit Construction Management, Co., Ltd., Beijing 100068, China
Liang Li: Beijing Rail Transit Construction Management, Co., Ltd., Beijing 100068, China
Haodong Yin: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Sustainability, 2023, vol. 15, issue 3, 1-21

Abstract: Timetable optimization for urban subways is aimed at improving the transportation service. In congested subway systems, the effects of crowding at stations and inside the vehicles have not been properly addressed in timetabling. Moreover, it is difficult to show the time of values in different riding conditions. In this paper, we consider the passenger-travel process as a physical activity expending energy and formulate a travel energy expenditure function for a heavily congested urban subway corridor. A timetable optimization model is proposed to minimize the total energy expenditure, including waiting on the platform and travelling in the vehicle. We develop a heuristic generic algorithm to solve the optimization problem through a special binary coding method. The model is applied to the Yi-zhuang line in the Beijing subway system to obtain a passenger-oriented energy-minimizing timetable. Compared with using the existing timetable, we find a 20% reduction in average energy expenditure per passenger and a RMB 47,500 increase in social profits as the result of the timetable optimization.

Keywords: timetable optimization; urban subway; passenger energy expenditure; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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