Volume 32 Issue 5
Sep.  2023
Turn off MathJax
Article Contents
CHENG Fanglin, TANG Tao, SU Shuai, et al., “Optimization on the Dynamic Train Coupling Process in High-Speed Railway,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1002-1010, 2023, doi: 10.23919/cje.2022.00.189
Citation: CHENG Fanglin, TANG Tao, SU Shuai, et al., “Optimization on the Dynamic Train Coupling Process in High-Speed Railway,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1002-1010, 2023, doi: 10.23919/cje.2022.00.189

Optimization on the Dynamic Train Coupling Process in High-Speed Railway

doi: 10.23919/cje.2022.00.189
Funds:  This work was supported by the Talent Fund of Beijing Jiaotong University (2021RC271), the National Natural Science Foundation of China (52172322, U22A2046), the Foundation of China State Railway Group Co., Ltd. (L2021G003, L2022G010), the Beijing Natural Science Foundation (L201004, L191015), and the State Key Laboratory of Rail Traffic Control and Safety (RCS2022ZZ003, RCS2022ZI002)
More Information
  • Author Bio:

    Fanglin CHENG was born in 1996. She received the B.E. degree from Beijing Jiaotong University, Beijing, China in 2019. She received the M.E. degreee with the State Key Laboratory of Rail Traffic Control and Safety in Beijing Jiaotong University, Beijing, China, in 2022. Her current research interests include intelligent control, machine learning, and energy-efficient train operation in railway system. (Email: 19120200@bjtu.edu.cn)

    Tao TANG was born in 1963. He received the Ph.D. degree in control theory from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 1991. He is currently a Professor and the Director of the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. His research interests include communication-based train control, high-speed train control system, and intelligent transportation systems. (Email: ttang@bjtu.edu.cn)

    Shuai SU (corresponding author) was born in 1987. He received the Ph.D. degree from Beijing Jiaotong University, Beijing, China in 2016. He is currently working as the Deputy Director with the Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University. His current research interests include energyefficient operation and control in railway systems, intelligent train control and dispatching. (Email: shuaisu@bjtu.edu.cn)

    Jun MENG was born in 1983. He received the M.S. degree from Beijing Jiaotong University, Beijing, China, in 2018. Currently he works as an Associate Researcher in Signal & Communication Research Institute, China Academy of Railway Sciences Corporation Limited. His research interests include intelligent train control and information technology in railway system. (Email: mengjun1983@126.com)

  • Received Date: 2022-07-02
  • Accepted Date: 2022-12-03
  • Available Online: 2023-01-14
  • Publish Date: 2023-09-05
  • This work focuses on the driving strategy optimization problem of a scenario in which two trains come from two branches under virtual coupling, aiming at going through the junction area efficiently. A distance-discrete optimal control model is constructed. The optimization objective is to maximize the trip time during which the two trains operate in coupled state. The line conditions, dynamic properties of the trains and the safety protection constraints are considered. The nonlinear constraints are converted into linear constraints with piecewise affine function and logical variables, and the proposed problem is converted into mixed integer linear programming (MILP) problem which can be solved by existing solvers such as Cplex. Four simulation experiments are conducted to verify the effectiveness of MILP. The dynamic programming (DP) algorithm is used as the benchmark algorithm in the case study. Compared with DP algorithm in small state space, MILP has better performance since it shortens the coupling time. Moreover, the improvement of line capacity of virtual coupling is 35.42% compared with the fixed blocking system.
  • loading
  • [1]
    J. Aoun, E. Quaglietta, and R. M. P. Goverde, “Investigating market potentials and operational scenarios of virtual coupling railway signaling,” Transportation Research Record:Journal of the Transportation Research Board, vol.2674, no.8, pp.799–812, 2020. doi: 10.1177/0361198120925074
    [2]
    E. Quaglietta, M. Wang, and R. M. P. Goverde, “A multi-state train-following model for the analysis of virtual coupling railway operations,” Journal of Rail Transport Planning & Management, vol.15, article no.100195, 2020. doi: 10.1016/j.jrtpm.2020.100195
    [3]
    U. Bock and G. Bikker, “Design and development of a future freight train concept-“Virtually Coupled Train Formations”,” IFAC Proceedings Volumes, vol.33, no.9, pp.395–400, 2000. doi: 10.1016/S1474-6670(17)38176-4
    [4]
    S. Konig and E. Schnieder, “Modeling and simulation of an operation concept for future rail traffic,” in Proceedings of 2001 IEEE Intelligent Transportation Systems, Oakland, CA, USA, pp.808–812, 2001.
    [5]
    L. Liu, P. Wang, B. Zhang, et al., “Coordinated control method of virtually coupled train formation based on multi agent system,” in Proceeding of the Second International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, Mount Emei, China, pp.225–233, 2018.
    [6]
    L. Liu, P. Wang, W. Wei, et al., “Intelligent dispatching and coordinated control method at railway stations for virtually coupled train sets,” in Proceeding of 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, pp.607–612, 2019.
    [7]
    J. Felez, Y. Kim, and F. Borrelli, “A model predictive control approach for virtual coupling in railways,” IEEE Transactions on Intelligent Transportation Systems, vol.20, no.7, pp.2728–2739, 2019. doi: 10.1109/TITS.2019.2914910
    [8]
    J. F. She, K. C. Li, L. Yuan, et al., “Cruising control approach for virtually coupled train set based on model predictive control,” in Proceeding of the 23rd International Conference on Intelligent Transportation Systems, Rhodes, Greece, pp.1–6, 2020.
    [9]
    Z. Y. Wu, C. H. Gao, and T. Tang, “A virtually coupled metro train platoon control approach based on model predictive control,” IEEE Access, vol.9, pp.56354–56363, 2021. doi: 10.1109/ACCESS.2021.3071820
    [10]
    J. Park, B. H. Lee, and Y. Eun, “Virtual coupling of railway vehicles: Gap reference for merge and separation, robust control, and position measurement,” IEEE Transactions on Intelligent Transportation Systems, vol.23, no.2, pp.1085–1096, 2022. doi: 10.1109/TITS.2020.3019979
    [11]
    C. Di Meo, M. Di Vaio, F. Flammini, et al., “ERTMS/ETCS virtual coupling: Proof of concept and numerical analysis,” IEEE Transactions on Intelligent Transportation Systems, vol.21, no.6, pp.2545–2556, 2020. doi: 10.1109/TITS.2019.2920290
    [12]
    Z. D. Song, X. N. Xu, H. Li, et al., “Study on virtual-coupling-orientated train control technique,” Railway Standard Design, vol.63, no.6, pp.155–159, 2019. (in Chinese) doi: 10.13238/j.issn.1004-2954.201808230001
    [13]
    Y. F. Liu, Y. Zhou, S. Su, et al., “An analytical optimal control approach for virtually coupled high-speed trains with local and string stability,” Transportation Research Part C: Emerging Technologies, vol.125, article no.102886, 2021. doi: 10.1016/j.trc.2020.102886
    [14]
    Y. Cao, J. K. Wen, and L. C. Ma, “Tracking and collision avoidance of virtual coupling train control system,” Future Generation Computer Systems, vol.120, pp.76–90, 2021. doi: 10.1016/j.future.2021.02.014
    [15]
    W. T. Liu, S. Su, T. Tang, et al., “A DQN-based intelligent control method for heavy haul trains on long steep downhill section,” Transportation Research Part C: Emerging Technologies, vol.129, article no.103249, 2021. doi: 10.1016/j.trc.2021.103249
    [16]
    S. Su, X. K. Wang, Y. Cao, et al., “An energy-efficient train operation approach by integrating the metro timetabling and eco-driving,” IEEE Transactions on Intelligent Transportation Systems, vol.21, no.10, pp.4252–4268, 2020. doi: 10.1109/TITS.2019.2939358
    [17]
    Q. Wang, M. Chai, H. J. Liu, et al., “Optimized control of virtual coupling at junctions: a cooperative game-based approach,” Actuators, vol.10, no.9, article no.207, 2021. doi: 10.3390/ACT10090207
    [18]
    H. Zhao and X. W. Dai, “Event-triggered adaptive control for multiple high-speed trains with deception attacks in bottleneck sections,” Information Sciences, vol.547, pp.470–481, 2021. doi: 10.1016/j.ins.2020.08.012
    [19]
    Y. B. Zhao and P. Ioannou, “Positive train control with dynamic headway based on an active communication system,” IEEE Transactions on Intelligent Transportation Systems, vol.16, no.6, pp.3095–3103, 2015. doi: 10.1109/TITS.2015.2435515
    [20]
    R. F. Liu and I. M. Golovitcher, “Energy-efficient operation of rail vehicles,” Transportation Research Part A: Policy and Practice, vol.37, no.10, pp.917–932, 2003. doi: 10.1016/j.tra.2003.07.001
    [21]
    S. Su, T. Tang, J. Xun, et al., “Design of running grades for Energy-Efficient train regulation: A case study for Beijing Yizhuang Line,” IEEE Intelligent Transportation Systems Magazine, vol.13, no.2, pp.189–200, 2021. doi: 10.1109/MITS.2019.2907681
    [22]
    B. Y. Su, T. Tang, S. Su, et al., “Integrated rescheduling of train timetables and rolling stock circulation for metro line disturbance management: a Q-learning-based approach,” Engineering Optimization, in press, 2023.
    [23]
    R. Franke, M. Meyer, and P. Terwiesch, “Optimal control of the driving of trains,” Automatisierungstechnik, vol.50, no.12, pp.606–614, 2002. doi: 10.1524/auto.2002.50.12.606
    [24]
    Y. H. Wang, B. De Schutter, T. J. J. van den Boom, et al., “Optimal trajectory planning for trains-A pseudospectral method and a mixed integer linear programming approach,” Transportation Research Part C: Emerging Technologies, vol.29, pp.97–114, 2013. doi: 10.1016/j.trc.2013.01.007
    [25]
    R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA, pp. 73–88, 1998.
    [26]
    H. Williams, “Model building in mathematical programming,” Mathematics and Computers in Simulation, vol.33, no.1, pp.78–79, 1991.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(5)

    Article Metrics

    Article views (505) PDF downloads(146) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return