Research on the Integrated Optimization of Timetable and High-Speed Train Routing Considering the Coordination Between Weekdays and Holidays

Zhiwen Zhang, Fengqian Guo, Wenjia Deng and Junhua Chen ()
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Zhiwen Zhang: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Fengqian Guo: School of Transportation, Southeast University, Nanjing 211189, China
Wenjia Deng: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Junhua Chen: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2024, vol. 12, issue 23, 1-23

Abstract: In recent years, passenger holiday travel momentum continues to increase, which proposes a challenge to the refined transportation organization of China’s high-speed railway. In order to save the cost of transportation organization, this paper proposes a collaborative optimization method using a high-speed railway train diagram and Electric Multiple Unit (EMU) routing considering the coordination of weekdays and holidays. Based on the characteristics of the train diagram and EMU routing, this method optimizes the EMU routing synchronously when compiling the train diagram. By constructing a space–time–state network, considering the constraints of train headway, operation conflict, and EMU maintenance, a collaborative optimization model of the train diagram and EMU routing considering the coordination of weekdays and holidays is established. This research combines the actual operation data to verify the model and algorithm. Based on five consecutive days of holidays, a seven-day transportation plan covering before and after the holidays and during the holidays is designed, and a case study is carried out. The results show that the proposed collaborative optimization theory has practical significance in the application scenarios of high-speed railway holidays.

Keywords: train diagram; EMU routing; collaborative optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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