×

Description and control of railway traffic flow under a moving block system. (English) Zbl 07715779

Summary: A moving block system (MBS) will contribute meaningfully to the railway. The advanced system can allow trains to operate closely as a platoon and significantly reduce train headway. Any minor speed disturbance of a train may lead to a chain of changes in operating status for some following trains in a platoon. However, any minor speed disturbance of a train may lead to a domino effect of changes in operating status for subsequent trains. This highlights the need for a thorough understanding of rail traffic flow behaviors under the MBS. Unfortunately, few studies have focused on this area. This study develops a micro-simulation model to describe train movements under MBS, and study in detail two typical railway operation scenarios: steep descending grade and arrival. On this basis, congestion phenomenon and bottleneck effect were explained in combination with the macroscopic rail fundamental diagram (RFD).
Moreover, the strong controllability of trains and the predictability of shockwaves make it achievable and promising to control the rail traffic flow under the MBS. This study illustrates rail variable speed limit (RVSL) control in the RFD, and it dynamically provides trains with advisory speed limits to resolve delays and maintain a higher flow at bottlenecks. Two congestion scenarios are considered to evaluate its effectiveness, including the temporary excessive demand upstream of a descending grade and the arrival delay. The results demonstrate that the proper imposition of RVSL control reduces the number of affected trains, delay time, and energy consumption in both scenarios.

MSC:

82-XX Statistical mechanics, structure of matter
Full Text: DOI

References:

[1] Theeg, G.; Vlasenko, S., Railway Signalling & Interlocking: International Compendium (2009), Eurail Press: Eurail Press Hamburg
[2] Harrod, S. S., A tutorial on fundamental model structures for railway timetable optimization, Surv. Oper. Res. Manag. Sci., 17, 85-96 (2012)
[3] Luan, X. J.; Wang, Y. H.; De Schutter, B.; Meng, L. Y.; Lodewijks, G.; Corman, F., Integration of real-time traffic management and train control for rail networks - part 1: Optimization problems and solution approaches, Transp. Res. B, 115, 41-71 (2018)
[4] Treiber, M.; Kesting, A., Traffic Flow Dynamics: Data, Models and Simulation (2013), Springer
[5] Ni, D. H., Traffic Flow Theory (2016), Butterworth-Heinemann
[6] Qian, Y. S.; Zeng, J. W.; Wang, N.; Zhang, J. L.; Wang, B. B., A traffic flow model considering influence of car-following and its echo characteristics, Nonlinear Dynam., 89, 1099-1109 (2017)
[7] Zeng, J. W.; Qian, Y. S.; Mi, P. F.; Zhang, C. Y.; Yin, F.; Zhu, L. P.; Xu, D. J., Freeway traffic flow cellular automata model based on mean velocity feedback, Physica A, 562 (2021) · Zbl 07542645
[8] Zeng, J. W.; Qian, Y. S.; Lv, Z. W.; Yin, F.; Zhu, L. P.; Zhang, Y. Z.; Xu, D. J., Expressway traffic flow under the combined bottleneck of accident and on-ramp in framework of Kerner’s three-phase traffic theory, Physica A, 574 (2021) · Zbl 07460583
[9] Cheng, Q. X.; Liu, Z. Y.; Lin, Y. Q.; Zhou, X. S., An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship, Transp. Res. B, 153, 246-271 (2021)
[10] Li, K. P.; Gao, Z. Y.; Ning, B., Cellular automaton model for railway traffic, J. Comput. Phys., 209, 179-192 (2005) · Zbl 1094.90007
[11] Qian, Y. S.; Da, C.; Zeng, J. W.; Wang, X. X.; Zhang, Y. Z.; Xu, D. J., A bidirectional quasi-moving block cellular automaton model for single-track railways, Physica A, 598 (2022)
[12] Jing, X.; Bin, N.; Ke-Ping, L., Station model for rail transit system using cellular automata, Commun. Theor. Phys., 51, 595-599 (2009)
[13] Xun, J.; Ning, B.; Li, K. P.; Zhang, W. B., The impact of end-to-end communication delay on railway traffic flow using cellular automata model, Transp. Res. Part C-Emerg. Technol., 35, 127-140 (2013)
[14] Liu, Y. T.; Cao, C. X.; Zhou, Y. L.; Feng, Z. Y., A real-time control method-based simulation for high-speed trains on large-scale rail network, Internat. J. Modern Phys. C, 28, Article 1750126 pp. (2017)
[15] Xu, Y.; Jia, B.; Li, M. H.; Li, X. G., An improved discrete-time model for heterogeneous high-speed train traffic flow, Commun. Theor. Phys., 65, 381-392 (2016) · Zbl 1335.90011
[16] Quaglietta, E.; Wang, M.; Goverde, R. M.P., A multi-state train-following model for the analysis of virtual coupling railway operations, J. Rail Transp. Plan. Manag., 15, Article 100195 pp. (2020)
[17] Bando, M.; Hasebe, K.; Nakayama, A.; Shibata, A.; Sugiyama, Y., Dynamical model of traffic congestion and numerical simulation, Phys. Rev. E, 51, 1035-1042 (1995)
[18] Wei, Y. G.; Avci, C.; Liu, J. T.; Belezamo, B.; Aydin, N.; Li, P. F.; Zhou, X. S., Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models, Transp. Res. B, 106, 102-129 (2017)
[19] Helly, W., Simulation of bottlenecks in single lane traffic flow, (Proceedings of the Symposium on Theory of Traffic Flow, Research Laboratories, General Motors (1961)), 207-238
[20] Ye, J. J.; Li, K. P.; Jin, X. M., Simulating train movement in an urban railway based on an improved car-following model, Chin. Phys. B, 22 (2013)
[21] Liu, R., Simulation model of speed control for the moving-block systems under ERTMS level 3, (IEEE International Conference on Intelligent Rail Transportation (2016)), 322-327
[22] Felez, J.; Kim, Y.; Borrelli, F., A model predictive control approach for virtual coupling in railways, Ieee Trans. Intell. Transp. Syst., 20, 2728-2739 (2019)
[23] Sogin, S. L., Simulations of mixed use rail corridors: how infrastructure affects interactions among train types, (University of Illinois At Urbana-Champaign, Urbana (2013))
[24] Seo, T.; Wada, K.; Fukuda, D., A macroscopic and dynamic model of urban rail transit with delay and congestion, (Transportation Research Board 96th Annual Meeting (2017))
[25] Zhang, J.; Wada, K., Fundamental diagram of urban rail transit: An empirical investigation by boston’s subway data, (8th Symposium of the European Association for Research in Transportation (2019), Budapest University of Technology and Economics)
[26] Cuniasse, P.-A.; Buisson, C.; Rodriguez, J.; Teboul, E.; de Almeida, D., Analyzing railroad congestion in a dense urban network through the use of a road traffic network fundamental diagram concept, Public Transp., 7, 355-367 (2015)
[27] Corman, F.; Henken, J.; Keyvan-Ekbatani, M., Macroscopic fundamental diagrams for train operations - are we there yet?, (6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2019))
[28] Diaz de Rivera, A.; Dick, C. T., Illustrating the implications of moving blocks on railway traffic flow behavior with fundamental diagrams, Transp. Res. Part C-Emerg. Technol., 123 (2021)
[29] Albrecht, T.; Binder, A.; Gassel, C., Applications of real-time speed control in rail-bound public transportation systems, IET Intell. Transp. Syst., 7, 305-314 (2013)
[30] Xu, P. J.; Corman, F.; Peng, Q. Y.; Luan, X. J., A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system, Transp. Res. B, 104, 638-666 (2017)
[31] Hiraguri, S.; Hirao, Y.; Watanabe, I.; Tomii, N.; Hase, S., Advanced train and traffic control based on prediction of train movement, Jsme Int. J. Ser. C-Mech. Syst. Mach. Elements and Manuf., 47, 523-528 (2004)
[32] Kunimatsu, T.; Terasawa, T.; Takeuchi, Y., Development of train operation simulator under moving block and prediction control, IEEJ Trans. Ind. Appl., 138, 313-322 (2018)
[33] Carlson, R. C.; Papamichail, I.; Papageorgiou, M.; Messmer, A., Optimal motorway traffic flow control involving variable speed limits and ramp metering, Transp. Sci., 44, 238-253 (2010)
[34] Chen, D. J.; Ahn, S.; Hegyi, A., Variable speed limit control for steady and oscillatory queues at fixed freeway bottlenecks, Transp. Res. B, 70, 340-358 (2014)
[35] Hegyi, A.; Hoogendoorn, S. P.; Schreuder, M.; Stoelhorst, H.; Viti, F., Specialist: A dynamic speed limit control algorithm based on shock wave theory, (11th International IEEE Conference on Intelligent Transportation Systems (2008)), 827-832
[36] Han, Y.; Hegyi, A.; Yuan, Y. F.; Hoogendoorn, S.; Papageorgiou, M.; Roncoli, C., Resolving freeway jam waves by discrete first-order model-based predictive control of variable speed limits, Transp. Res. Part C-Emerg. Technol., 77, 405-420 (2017)
[37] Nishi, R.; Tomoeda, A.; Shimura, K.; Nishinari, K., Theory of jam-absorption driving, Transp. Res. B, 50, 116-129 (2013)
[38] He, Z. B.; Zheng, L.; Song, L. Y.; Zhu, N., A jam-absorption driving strategy for mitigating traffic oscillations, IEEE Trans. Intell. Transp. Syst., 18, 802-813 (2017)
[39] Zheng, Y.; Zhang, G.; Li, Y.; Li, Z., Optimal jam-absorption driving strategy for mitigating rear-end collision risks with oscillations on freeway straight segments, Accid. Anal. Prev., 135, Article 105367 pp. (2020)
[40] China, N. R.A.o., Railway train traction calculation-part 1: Trains with locomotives, in (2018)
[41] Xun, J.; Tang, T.; Ning, B., Optimization of speed profile for delayed train entering station, (Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics (2011)), 428-433
[42] Asuka, M.; Kataoka, K.; Komaya, K.; Nishida, S., Automatic train operation using autonomic prediction of train runs, Electr. Eng. Japan, 175, 65-73 (2011)
[43] Talebpour, A.; Mahmassani, H. S., Influence of connected and autonomous vehicles on traffic flow stability and throughput, Transp. Res. Part C-Emerg. Technol., 71, 143-163 (2016)
[44] Newell, G. F., A simplified car-following theory: a lower order model, Transp. Res. B, 36, 195-205 (2002)
[45] Chen, D.; Ahn, S.; Laval, J.; Zheng, Z., On the periodicity of traffic oscillations and capacity drop: The role of driver characteristics, Transp. Res. B, 59, 117-136 (2014)
[46] Yang, H.; Rakha, H., Feedback control speed harmonization algorithm: Methodology and preliminary testing, Transp. Res. Part C-Emerg. Technol., 81, 209-226 (2017)
[47] Chen, J. Z.; Zhou, Y.; Liang, H., Effects of ACC and CACC vehicles on traffic flow based on an improved variable time headway spacing strategy, IET Intell. Transp. Syst., 13, 1365-1373 (2019)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.