Energy Efficient Metro Train Running Time Rescheduling Model for Fully Automatic Operation Lines
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 7
Abstract
Reducing energy consumption without degrading the normal operation of metro trains and service quality has received increasing attention. Besides, fully automatic operation (FAO), for which no drivers and crew attendants are needed and all functions are controlled automatically, has been applied as a new generation train operation integrated control technology to achieve better performance. In this paper, a two-level energy-efficient optimization approach is proposed based on the characteristics of the FAO system. First, we formulate a single train trajectory optimization model and develop a genetic algorithm to calculate the optimal speed curves with variable running time. The Pareto frontier that represents the relationship between energy consumption and running time can be obtained. Second, we propose a sensitivity analysis method to distribute the total running time among different station segments based on the Pareto solutions in Level 1. Furthermore, the global optimal solution can be obtained. Finally, a case study of the Nanning Rail Transit Line 5 demonstrates that an optimal distribution of running time leads to extra energy savings compared to the original timetable.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all of the data, models, and code that support the finding of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research was supported by the National Natural Science Foundation of China (Grant No. 517650060, Natural Science Foundation of Guangxi Province of China (Grant No. 2017GXNSFDA198012), Nanning Excellent Young Scientist Program (Grant No. RC20190204), Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund (Grant No. 19-050-44-S015), and the Innovation Project of Guangxi Graduate Education (Grant No. YCSW2020017). Major Science and Technology Project of Guangxi Province of China (Grant No. Guike AA20302010).
References
Ahmadi, S., A. Dastfan, and M. Assili. 2018. “Energy saving in metro systems: Simultaneous optimization of stationary energy storage systems and speed profiles.” J. Rail Transp. Plann. Manage. 8 (1): 78–90. https://doi.org/10.1016/j.jrtpm.2018.03.003.
Cao, Y., L. C. Ma, S. Xiao, X. Zhang, and W. Xu. 2017. “Standard analysis for transfer delay in CTCS-3.” Chin. J. Electron. 26 (5): 1057–1063. https://doi.org/10.1049/cje.2017.08.024.
Cao, Y., L. C. Ma, and Y. Z. Zhang. 2019a. “Application of fuzzy predictive control technology in automatic train operation.” Cluster Comput. 22: 14135–14144. https://doi.org/10.1007/s10586-018-2258-0.
Cao, Y., Z. C. Wang, F. Liu, P. Li, and G. Xie. 2019b. “Bio-inspired speed curve optimization and sliding model tracking control for subway trains.” IEEE Trans. Veh. Technol. 68 (7): 6331–6342. https://doi.org/10.1109/TVT.2019.2914936.
Carvajal-Carreño, W., A. P. Cucala, and A. Fernández-Cardador. 2014. “Optimal design of energy-efficient ATO CBTC driving for metro lines based on NSGA-II with fuzzy parameters.” Eng. Appl. Artif. Intell. 36 (Nov): 164–177. https://doi.org/10.1016/j.engappai.2014.07.019.
Carvajal-Carreño, W., A. P. Cucala, and A. Fernández-Cardador. 2016. “Fuzzy train tracking algorithm for the energy efficient operation of CBTC equipped metro lines.” Eng. Appl. Artif. Intell. 53 (Aug): 19–31. https://doi.org/10.1016/j.engappai.2016.03.011.
Domínguez, M., A. Fernández-Cardador, A. P. Cucala, T. Gonsalves, and A. Fernández. 2014. “Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines.” Eng. Appl. Artif. Intell. 29 (Mar): 43–53. https://doi.org/10.1016/j.engappai.2013.12.015.
Domínguez, M., A. Carvajal-Carreño, A. P. Cucala, and R. R. Pecharroman. 2012. “Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles.” IEEE Trans. Autom. Sci. Eng. 9 (3): 496–504. https://doi.org/10.1109/TASE.2012.2201148.
Dong, H. R., B. Ning, and B. G. Cai, and Z. Hou. 2010. “Automatic train control system development and simulation for high-speed railways.” IEEE Circuits Syst. Mag. 10 (2): 6–18. https://doi.org/10.1109/MCAS.2010.936782.
Douglas, H., C. Roberts, S. Hillmansen, and F. Schmid. 2015. “An assessment of available measures to reduce traction energy use in railway networks.” Energy Convers. Manage. 106 (Dec): 1149–1165. https://doi.org/10.1016/j.enconman.2015.10.053.
Dullinger, C., W. Struckl, and M. Kozek. 2017. “Simulation-based multi-objective system optimization of train traction systems.” Simul. Modell. Pract. Theory 72 (Mar): 104–117. https://doi.org/10.1016/j.simpat.2016.12.008.
Fernández-Rodríguez, A., A. Fernández-Cardador, and A. P. Cucala. 2018. “Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization.” Simul. Modell. Pract. Theory 84 (May): 50–68. https://doi.org/10.1016/j.simpat.2018.01.006.
Fernández-Rodríguez, A., A. Fernández-Cardador, A. P. Cucala, M. Domínguez, and T. Gonsalves. 2015. “Design of robust and energy-efficient ATO speed profiles of metropolitan lines considering train load variations and delays.” IEEE Trans. Intell. Transp. Syst. 16 (4): 2061–2071. https://doi.org/10.1109/TITS.2015.2391831.
Fraszczyk, A., P. Brown, and S. Duan. 2015. “Public perception of driverless trains.” Urban Rail Transit 1 (Sep): 78–86. https://doi.org/10.1007/s40864-015-0019-4.
Gu, J. J., Z. B. Jiang, W. Fan, and J. Wu. 2020. “Real-time passenger flow anomaly detection considering typical time series clustered characteristics at metro stations.” J. Transp. Eng. Part A: Syst. 146 (4): 04020015. https://doi.org/10.1061/JTEPBS.0000333.
Haahr, J. T., D. Pisinger, and M. Sabbaghian. 2017. “A dynamic programming approach for optimizing train speed profiles with speed restrictions and passage points.” Transp. Res. Part B: Methodol. 99 (May): 167–182. https://doi.org/10.1016/j.trb.2016.12.016.
He, D. Q., Y. J. Yang, Y. J. Chen, J. Deng, S. Shan, J. Liu, and X. Li. 2020. “An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer.” Appl. Energy 264 (Apr): 114770. https://doi.org/10.1016/j.apenergy.2020.114770.
He, D. Q., Y. J. Yang, J. X. Zhou, et al. 2019. “Optimal control of metro energy conservation based on regenerative braking: A complex model study of trajectory and overlap time.” IEEE Access 7 (99): 68342–68358. https://doi.org/10.1109/ACCESS.2019.2918938.
Huang, K., J. J. Wu, X. Yang, Z. Gao, F. Liu, and Y. Zhu. 2019. “Discrete train speed profile optimization for urban rail transit: A data-driven model and integrated algorithms based on machine learning.” J. Adv. Transp. 4 (May): 1–17. https://doi.org/10.1155/2019/7258986.
Huang, Y. R., L. X. Yang, T. Tang, Z. Gao, and F. Cao. 2017. “Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks.” Energy 138 (Nov): 1124–1147. https://doi.org/10.1016/j.energy.2017.07.117.
Karvonen, H., I. Aaltonen, M. Wahlström, et al. 2011. “Hidden roles of the train driver: A challenge for metro automation.” Interact. Comput. 23 (4): 289–298. https://doi.org/10.1016/j.intcom.2011.04.008.
Kim, K., and S. I.-J. Chien. 2011. “Optimal train operation for minimum energy consumption considering track alignment, speed limit, and schedule adherence.” J. Transp. Eng. 137 (9): 665–674. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000246.
Kuppusamy, P., S. Venkatraman, C. A. Rishikeshan, and Y. C. A. Padmanabha Reddy. 2020. “Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems.” Phys. Commun. 42 (Oct): 101131. https://doi.org/10.1016/j.phycom.2020.101131.
Li, W. X., Q. Y. Peng, C. Wen, P. Wang, J. Lessan, and X. Xu. 2020. “Joint optimization of delay-recovery and energy-saving in a metro system: A case study from China.” Energy 202 (Jul): 117699. https://doi.org/10.1016/j.energy.2020.117699.
Liang, Y. C., H. Liu, C. Y. Qian, and G. Wang. 2019. “A modified genetic algorithm for multi-objective optimization on running curve of automatic train operation system using penalty function method.” Int. J. Intell. Transp. Syst. Res. 17 (Jan): 74–87. https://doi.org/10.1007/s13177-018-0158-6.
Lu, S. F., S. Hillmansen, T. K. Ho, and C. Roberts. 2013. “Single-train trajectory optimization.” IEEE Trans. Intell. Transp. Syst. 14 (2): 743–750. https://doi.org/10.1109/TITS.2012.2234118.
Mintsis E., E. I. Vlahogianni, and E. Mitsakis. 2020. “Dynamic eco-driving near signalized intersections: Systematic review and future research directions.” J. Transp. Eng. Part A: Syst. 146 (4): 04020018. https://doi.org/10.1061/JTEPBS.0000318.
Niu, H. M., and X. S. Zhou. 2013. “Optimizing urban rail timetable under time-depended demand and oversaturated conditions.” Transp. Res. Part C: Emerging Technol. 36 (Nov): 212–230. https://doi.org/10.1016/j.trc.2013.08.016.
Scheepmaker, G. M., and R. M. P. Goverde. 2015. “The interplay between energy-efficient train control and scheduled running time supplements.” J. Rail Transp. Plann. Manage. 5 (4): 225–239. https://doi.org/10.1016/j.jrtpm.2015.10.003.
Su, S., X. Li, T. Tang, and Z. Gao. 2013. “A subway train timetable optimization approach based on energy-efficient operation strategy.” IEEE Trans. Intell. Transp. Syst. 14 (2): 883–893. https://doi.org/10.1109/TITS.2013.2244885.
Teymourfar, R., B. Asaei, H. Iman-Eini, and R. Nejati fard. 2012. “Stationary super-capacitor energy storage system to save regenerative braking energy in a metro line.” Energy Convers. Manage. 56 (Apr): 206–214. https://doi.org/10.1016/j.enconman.2011.11.019.
Wang, H. F., T. Tang, C. Roberts, C. Gao, L. Chen, and F. Schmid. 2014. “A novel framework for supporting the design of moving block train control system schemes.” Proc. Inst. Mech. Eng. Part F: J. Rail Rapid Transit 228 (7): 784–793. https://doi.org/10.1177/0954409713495015.
Wang, P. L., and R. M. P. Goverde. 2019. “Multi-train trajectory optimization for energy-efficient timetabling.” Eur. J. Oper. Res. 272 (2): 621–635. https://doi.org/10.1016/j.ejor.2018.06.034.
Wang, Y. H., M. Zhang, J. Q. Ma, and X. Zhou. 2016. “Survey on driverless train operation for urban rail transit systems.” Urban Rail Transit 2 (Dec): 106–113. https://doi.org/10.1007/s40864-016-0047-8.
Watanabe, S., and T. Koseki. 2015. “Energy-saving train scheduling diagram for automatically operated electric railway.” J. Rail Transp. Plann. Manage. 5 (3): 183–193. https://doi.org/10.1016/j.jrtpm.2015.10.004.
Watanabe, S., T. Koseki, and E. Isobe. 2017. “Evaluation of automatic train operation design for energy saving based on the measured efficiency of a linear-motor train.” IEEJ Trans. Ind. Appl. 137 (6): 460–468. https://doi.org/10.1541/ieejias.137.460.
Wu, P., Q. Y. Wang, and X. Y. Feng. 2015. “Automatic train operation based on adaptive terminal sliding mode control.” Int. J. Autom. Comput. 12 (Apr): 142–148. https://doi.org/10.1007/s11633-015-0877-y.
Yin, J. T., D. W. Chen, and L. X. Li. 2014. “Intelligent train operation algorithms for subway by expert system and reinforcement learning.” IEEE Trans. Intell. Transp. Syst. 15 (6): 2561–2571. https://doi.org/10.1109/TITS.2014.2320757.
Yin, J. T., T. Tang, L. X. Yang, J. Xun, Y. Huang, and Z. Gao. 2017. “Research and development of automatic train operation for railway transportation systems: A survey.” Transp. Res. Part C 85 (Dec): 548–572. https://doi.org/10.1016/j.trc.2017.09.009.
Zhan, S. G., S. C. Wong, and S. M. Lo. 2020. “Social equity-based timetabling and ticket pricing for high-speed railway.” Transp. Res. Part A: Policy Pract. 137 (Jul): 165–186. https://doi.org/10.1016/j.tra.2020.04.018.
Zhang, H. R., L. M. Jia, L. Wang, and X. Xu. 2019. “Energy consumption optimization of train operation for railway systems: Algorithm development and real-world case study.” J. Cleaner Prod. 214 (Mar): 1024–1037. https://doi.org/10.1016/j.jclepro.2019.01.023.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Oct 9, 2020
Accepted: Feb 23, 2021
Published online: May 10, 2021
Published in print: Jul 1, 2021
Discussion open until: Oct 10, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.