A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Q. Zhang, J. Gao, Q. Wu, Q. He, L. Tie, W. Zhai, and S. Zhu, “A novel vibration-based self-adapting method to acquire real-time following distance for virtually coupled trains,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124326
Citation: Q. Zhang, J. Gao, Q. Wu, Q. He, L. Tie, W. Zhai, and S. Zhu, “A novel vibration-based self-adapting method to acquire real-time following distance for virtually coupled trains,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124326

A Novel Vibration-Based Self-Adapting Method to Acquire Real-Time Following Distance for Virtually Coupled Trains

doi: 10.1109/JAS.2024.124326
Funds:  This work was supported by the National Natural Science Foundation of China (52222217, 52388102, 52372435) and the major science and technology project of China Energy (GJNY-22-7)
More Information
  • Virtual coupling (VC) is an emerging technology for addressing the shortage of rail transportation capacity. As a crucial enabling technology, the VC-specific acquisition of train information, especially train following distance (TFD), is underdeveloped. In this paper, a novel method is proposed to acquire real-time TFD by analyzing the vibration response of the front and following trains, during which only onboard accelerometers and speedometers are required. In contrast to the traditional arts of train positioning, this method targets a relative position between two adjacent trains in VC operation, rather than the global positions of the trains. For this purpose, an adaptive system containing three strategies is designed to cope with possible adverse factors in train operation. A vehicle dynamics simulation of a heavy-haul railway is implemented for the evaluation of feasibility and performance. Furthermore, a validation is conducted using a set of data measured from in-service Chinese high-speed trains. The results indicate the method achieves satisfactory estimation accuracy using both simulated and actual data. It has favorable adaptability to various uncertainties possibly encountered in train operation. Additionally, the method is preliminarily proven to adapt to different locomotive types and even different rail transportation modes. In general, such a method with good performance, low-cost, and easy implementation is promising to apply.


  • loading
  • [1]
    E. Quaglietta, M. Wang, and R. M. P. Goverde, “A multi-state train-following model for the analysis of virtual coupling railway operations,” J. Rail Transp. Plann. Manage., vol. 15, p. 100195, Sep. 2020.
    W. Xu, C. Zhao, J. Cheng, Y. Wang, Y. Tang, T. Zhang, Z. Yuan, Y. Lv, and F.-Y. Wang, “Transformer-based macroscopic regulation for high-speed railway timetable rescheduling,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1822–1833, Sep. 2023. doi: 10.1109/JAS.2023.123501
    X. Wu, M. Yang, W. Lian, M. Zhou, H. Wang, and H. Dong, “Cascading delays for the high-speed rail network under different emergencies: A double layer network approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 2014–2025, Oct. 2023. doi: 10.1109/JAS.2022.105530
    Q. Wu, X. Ge, Q.-L. Han, and Y. Liu, “Railway virtual coupling: A survey of emerging control techniques,” IEEE Trans. Intell. Veh., vol. 8, no. 5, pp. 3239–3255, May 2023. doi: 10.1109/TIV.2023.3260851
    S. Stickel, M. Schenker, H. Dittus, P. Unterhuber, S. Canesi, V. Riquier, F. P. Ayuso, M. Berbineau, and J. Goikoetxea, “Technical feasibility analysis and introduction strategy of the virtually coupled train set concept,” Sci. Rep., vol. 12, no. 1, p. 4248, Mar. 2022. doi: 10.1038/s41598-022-08215-y
    J. Felez and M. A. Vaquero-Serrano, “Virtual coupling in railways: A comprehensive review,” Machines, vol. 11, no. 5, p. 521, Apr. 2023. doi: 10.3390/machines11050521
    J. Xun, Y. Li, R. Liu, Y. Li, and Y. Liu, “A survey on control methods for virtual coupling in railway operation,” IEEE Open J. Intell. Transp. Syst., vol. 3, pp. 838–855, Dec. 2022. doi: 10.1109/OJITS.2022.3228077
    X. Ge, S. Xiao, Q.-L. Han, X.-M. Zhang, and D. Ding, “Dynamic event-triggered scheduling and platooning control co-design for automated vehicles over vehicular ad-hoc networks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 31–46, Jan. 2022. doi: 10.1109/JAS.2021.1004060
    M. Xie, D. Ding, X. Ge, Q.-L. Han, H. Dong, and Y. Song, “Distributed platooning control of automated vehicles subject to replay attacks based on proportional integral observers,” IEEE/CAA J. Autom. Sinica, p. , 2022. doi: 10.1109/JAS.2022.105941
    M. Hu, L. Bu, Y. Bian, H. Qin, N. Sun, D. Cao, and Z. Zhong, “Hierarchical cooperative control of connected vehicles: From heterogeneous parameters to heterogeneous structures,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1590–1602, Sep. 2022. doi: 10.1109/JAS.2022.105536
    Shift2Rail Strategic Master Plan Version 1.0, 2015. [Online]. Available: http://ec.europa.eu/transport/modes/rail/doc/2015-03-31-decisionn4-2015-adoption-s2r-masterplan.pdf.
    C. Di Meo, M. Di Vaio, F. Flammini, R. Nardone, S. Santini, and V. Vittorini, “ERTMS/ETCS virtual coupling: Proof of concept and numerical analysis,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 6, pp. 2545–2556, Jun. 2020. doi: 10.1109/TITS.2019.2920290
    J. Aoun, E. Quaglietta, and R. M. P. Goverde, “Investigating market potentials and operational scenarios of virtual coupling railway signaling,” Transp. Res. Rec.: J. Transp. Res. Board, vol. 2674, no. 8, pp. 799–812, Jun. 2020. doi: 10.1177/0361198120925074
    M. Choi, H. Lee, and M. Kim, “Survey on V2X communication for the train integrity in the virtual coupling convoy,” in Proc. Int. Symp. Green Manufacturing and Applications, Gyeongju, Korea, 2017, pp. 1457.
    P. Fraga-Lamas, T. M. Fernández-Caramés, and L. Castedo, “Towards the internet of smart trains: A review on industrial IoT-connected railways,” Sensors, vol. 17, no. 6, p. 1457, Jun. 2017. doi: 10.3390/s17061457
    Q. Wu, X. Ge, Q.-L. Han, B. Wang, H. Wu, C. Cole, and M. Spiryagin, “Dynamics and control simulation of railway virtual coupling,” Veh. Syst. Dyn., vol. 61, no. 9, pp. 2292–2316, Aug. 2023. doi: 10.1080/00423114.2022.2105241
    J. Felez, Y. Kim, and F. Borrelli, “A model predictive control approach for virtual coupling in railways,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 7, pp. 2728–2739, Jul. 2019. doi: 10.1109/TITS.2019.2914910
    X. Ge, Q.-L. Han, Q. Wu, and X.-M. Zhang, “Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1234–1251, May 2023. doi: 10.1109/JAS.2022.105845
    X. Ge, Q.-L. Han, X.-M. Zhang, and D. Ding, “Communication resource-efficient vehicle platooning control with various spacing policies,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 362–376, Feb. 2024. doi: 10.1109/JAS.2023.123507
    J. Meng, R. Xu, D. Li, and X. Chen, “Combining the matter-element model with the associated function of performance indices for automatic train operation algorithm,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 1, pp. 253–263, Jan. 2019. doi: 10.1109/TITS.2018.2805917
    G. Muniandi and E. Deenadayalan, “Train distance and speed estimation using multi sensor data fusion,” IET Radar, Sonar Navig., vol. 13, no. 4, pp. 664–671, Apr. 2019. doi: 10.1049/iet-rsn.2018.5359
    S. Saidi, H. N. Koutsopoulos, N. H. M. Wilson, and J. Zhao, “Train following model for urban rail transit performance analysis,” Transp. Res. Part C: Emerging Technol., vol. 148, p. 104037, Mar. 2023. doi: 10.1016/j.trc.2023.104037
    D. Pan, Y. Zheng, J. Qiu, and L. Zhao, “Synchronous control of vehicle following behavior and distance under the safe and efficient steady-following state: Two case studies of high-speed train following control,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 5, pp. 1445–1456, May 2018. doi: 10.1109/TITS.2017.2729593
    W. Zhai, K. Wang, and C. Cai, “Fundamentals of vehicle-track coupled dynamics,” Veh. Syst. Dyn., vol. 47, no. 11, pp. 1349–1376, Oct. 2009. doi: 10.1080/00423110802621561
    C. Cole, M. Spiryagin, Q. Wu, and Y. Sun, “Modelling, simulation and applications of longitudinal train dynamics,” Veh. Syst. Dyn., vol. 55, no. 10, pp. 1498–1571, Jun. 2017. doi: 10.1080/00423114.2017.1330484
    Q. Wu, M. Spiryagin, and C. Cole, “Longitudinal train dynamics: An overview,” Veh. Syst. Dyn., vol. 54, no. 12, pp. 1688–1714, Sep. 2016. doi: 10.1080/00423114.2016.1228988
    K. Hwang, J. Cho, J. Park, D. Har, and S. Ahn, “Ferrite position identification system operating with wireless power transfer for intelligent train position detection,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 1, pp. 374–382, Jan. 2019. doi: 10.1109/TITS.2018.2797991
    B. Allotta, V. Colla, and M. Malvezzi, “Train position and speed estimation using wheel velocity measurements,” Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit, vol. 216, no. 3, pp. 207–225, May 2002. doi: 10.1243/095440902760213639
    Z. Xu, W. Wang, and Y. Sun, “Performance degradation monitoring for onboard speed sensors of trains,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1287–1297, Sep. 2012. doi: 10.1109/TITS.2012.2188629
    M. Lauer and D. Stein, “A train localization algorithm for train protection systems of the future,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 970–979, Apr. 2015.
    J. Marais, J. Beugin, and M. Berbineau, “A survey of GNSS-based research and developments for the european railway signaling,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 10, pp. 2602–2618, Oct. 2017. doi: 10.1109/TITS.2017.2658179
    A. Mirabadi, N. Mort, and F. Schmid. “Application of sensor fusion to railway systems,” in Proc. IEEE/SICE/RSJ Int. Conf. Multisensor Fusion and Integration for Intelligent Systems, Washington, USA, 1996, pp. 185–192.
    M. Malvezzi, B. Allotta, and M. Rinchi, “Odometric estimation for automatic train protection and control systems,” Veh. Syst. Dyn., vol. 49, no. 5, pp. 723–739, Apr. 2011. doi: 10.1080/00423111003721291
    J. Otegui, A. Bahillo, I. Lopetegi, and L. E. Díez, “Evaluation of experimental GNSS and 10-DOF MEMS IMU measurements for train positioning,” IEEE Trans. Instrum. Meas., vol. 68, no. 1, pp. 269–279, Jan. 2019. doi: 10.1109/TIM.2018.2838799
    K. Ko, I. Byun, W. Ahn, and W. Shin, “High-speed train positioning using deep kalman filter with 5G NR signals,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 15993–16004, Sep. 2022. doi: 10.1109/TITS.2022.3146932
    Y. Zhou, Q. Chen, R. Wang, G. Jia, and X. Niu, “Onboard train localization based on railway track irregularity matching,” IEEE Trans. Instrum. Meas., vol. 71, p. 9501013, Jan. 2022.
    Q. Chen, Y. Zhou, B. Fang, Q. Zhang, and X. Niu, “Experimental study on the potential of vehicle’s attitude response to railway track irregularity in precise train localization,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 20452–20463, Nov. 2022. doi: 10.1109/TITS.2022.3174884
    D. Chen, X. Han, R. Cheng, and L. Yang, “Position calculation models by neural computing and online learning methods for high-speed train,” Neural Comput. Appl., vol. 27, no. 6, pp. 1617–1628, Aug. 2016. doi: 10.1007/s00521-015-1960-6
    D. Chen, L. Wang, and L. Li, “Position computation models for high-speed train based on support vector machine approach,” Appl. Soft Comput., vol. 30, pp. 758–766, May 2015. doi: 10.1016/j.asoc.2015.01.017
    L. Xu, W. Zhai, and J. Gao, “A probabilistic model for track random irregularities in vehicle/track coupled dynamics,” Appl. Math. Modell., vol. 51, pp. 145–158, Nov. 2017. doi: 10.1016/j.apm.2017.06.027
    T. Engelberg, “Design of a correlation system for speed measurement of rail vehicles,” Measurement, vol. 29, no. 2, pp. 157–164, Mar. 2001. doi: 10.1016/S0263-2241(00)00043-9
    T. Mei and H. Li, “A novel approach for the measurement of absolute train speed,” Veh. Syst. Dyn., vol. 46, no. S1, pp. 705–715, Sep. 2008.
    Q. Chen, X. Ge, Z. Shi, L. Ling, X. Hu, Y. Hu, and K. Wang, “Measurement of vehicle speed based on the GCC algorithm and its application in anti-slip control,” Measurement, vol. 219, p. 113298, Sep. 2023. doi: 10.1016/j.measurement.2023.113298
    Z.-W. Li, X.-Z. Liu, and Y.-L. He, “Identification of temperature-induced deformation for HSR slab track using track geometry measurement data,” Sensors, vol. 19, no. 24, p. 5446, Dec. 2019. doi: 10.3390/s19245446
    Y. Ren, S. Qu, J. Yang, Q. Li, B. Zhu, W. Zhai, and S. Zhu, “An efficient three-dimensional dynamic stiffness-based model for predicting subway train-induced building vibrations,” J. Build. Eng., vol. 76, p. 107239, Oct. 2023. doi: 10.1016/j.jobe.2023.107239
    Z. Zhai, C. Cai, and S. Zhu, “Implementation of Timoshenko curved beam into train-track-bridge dynamics modelling,” Int. J. Mech. Sci., vol. 247, p. 108158, Jun. 2023. doi: 10.1016/j.ijmecsci.2023.108158
    Q. Xie, G. Tao, S. M. Lo, X. Yang, and Z. Wen, “A data-driven convolutional regression scheme for on-board and quantitative detection of rail corrugation roughness,” Wear, vol. 524-525, p. 204770, Jul. 2023. doi: 10.1016/j.wear.2023.204770
    Q. Xie, G. Tao, B. He, and Z. Wen, “Rail corrugation detection using one-dimensional convolution neural network and data-driven method,” Measurement, vol. 200, p. 111624, Aug. 2022. doi: 10.1016/j.measurement.2022.111624
    C. Ward, P. Weston, E. J. C. Stewart, H. Li, R. M. Goodall, R. Goodall, T. X. Mei, G. Charles, and R. Dixon, “Condition monitoring opportunities using vehicle-based sensors,” Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit, vol. 225, no. 2, pp. 202–218, Feb. 2011. doi: 10.1177/09544097JRRT406


    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(17)  / Tables(1)

    Article Metrics

    Article views (33) PDF downloads(7) Cited by()


    DownLoad:  Full-Size Img  PowerPoint