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Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model

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  • Qiuchi Xue

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Xin Yang

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Jianjun Wu

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Huijun Sun

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Haodong Yin

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Yunchao Qu

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

Abstract

At present, most urban rail transit systems adopt an operation mode with a single long routing. The departure frequency is determined by the maximum section passenger flow. However, when the passenger flow varies greatly within different sections, this mode will lead to a low load factor in some sections, resulting in a waste of capacity. In view of this situation, this paper develops a nonlinear integer programming model to determine an optimal timetable with a balanced scheduling mode, where the wasted capacity at a constant departure frequency can be reduced with a slight increase in passenger waiting time. Then, we simplify the original model into a single-objective integer optimization model through normalization. A genetic algorithm is designed to find the optimal solution. Finally, a numerical example is presented based on real-world passenger and operation data from Beijing Metro Line 4. The results show that the double-routing optimization model can reduce wasted capacity by 9.5%, with a 4.5% increase in passenger waiting time, which illustrates the effectiveness of this optimization model.

Suggested Citation

  • Qiuchi Xue & Xin Yang & Jianjun Wu & Huijun Sun & Haodong Yin & Yunchao Qu, 2019. "Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model," Sustainability, MDPI, vol. 11(13), pages 1-26, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3701-:d:246067
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    References listed on IDEAS

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    2. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    4. Xuan Li & Toshiyuki Yamamoto & Tao Yan & Lili Lu & Xiaofei Ye, 2020. "First Train Timetabling for Urban Rail Transit Networks with Maximum Passenger Transfer Satisfaction," Sustainability, MDPI, vol. 12(10), pages 1-22, May.

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