Railway transport system modelling approach for robustness analysis
Abstract
The article presents an approach to train traffic modelling that allows for the analysis of how railway networks respond to various disturbances, including increased traffic and disturbance events. It discusses different methods of reconfiguration actions in key points of the railway network, which helps reduce delay propagation in the transport system. The 1st part covers building simulation models, which include defining infrastructure, setting train routes, configuring rolling stock, and disturbance scenarios, enabling the analysis of various disruptive events. The simulations allow for testing disturbance scenarios with minimal downtime risk without interfering with the real-world environment. The study results identified key system parameters generating the largest delays, such as platform availability, signaling, and the number of block sections. Probability density distributions for event intervals and durations were analyzed. The Kolmogorov–Smirnov test was used to confirm the fit of empirical distributions with theoretical ones, which were then implemented in the model of railway line No 271, running from Wrocław to Żmigród (Poland). As part of the reconfiguration of this railway line, new platforms were added, the time required for route setting was reduced, and the number of block sections was increased. These actions significantly reduced average delays, improved line capacity, and enhanced the robustness of the railway transport system against disturbances. The reconfiguration effectively reduced delays in areas causing significant time exceedances above 359 s, which was recognized in the Polish railway network as critical.
First published online 3 January 2025
Keyword : timetable, robustness, simulation, rail transport, railway line
How to Cite
Wolniewicz, Łukasz. (2024). Railway transport system modelling approach for robustness analysis. Transport, 39(4), 287–301. https://doi.org/10.3846/transport.2024.22877
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Khoshniyat, F.; Peterson, A. 2017. Improving train service reliability by applying an effective timetable robustness strategy, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 21(6): 525–543. https://doi.org/10.1080/15472450.2017.1326114
Kierzkowski, A.; Kisiel, T. 2015a. Functional readiness of the check-in desk system at an airport, Advances in Intelligent Systems and Computing 365: 223–233. https://doi.org/10.1007/978-3-319-19216-1_21
Kierzkowski, A.; Kisiel, T. 2015b. Modelling the passenger flow at an airport terminal to increase the safety level, in International Conference on Military Technologies (ICMT) 2015, 19–21 May 2015, Brno, Czechia, 1–7. https://doi.org/10.1109/MILTECHS.2015.7153693
Kierzkowski, A.; Kisiel, T. 2015c. Simulation model of logistic support for functioning of ground handling agent, taking into account a random time of aircrafts arrival, in International Conference on Military Technologies (ICMT) 2015, 19–21 May 2015, Brno, Czechia, 1–6. https://doi.org/10.1109/MILTECHS.2015.7153694
Li, K.; Gao, Z. 2007. An improved equation model for the train movement, Simulation Modelling Practice and Theory 15(9): 1156–1162. https://doi.org/10.1016/j.simpat.2007.07.006
Liang, Y. J.; Yuan, Z. Z. 2014. Assignment performance at Beijing south railway station based on Anylogic simulation, Advanced Materials Research 989–994: 2283–2287. https://doi.org/10.4028/www.scientific.net/AMR.989-994.2283
Liu, L.; Chen, H. 2020. Pedestrian emergency evacuation strategy in subway station based on AnyLogic, in CICTP 2020: Transportation Evolution Impacting Future Mobility, 14–16 August 2020, Xi’an, China, 3524–3533. https://doi.org/10.1061/9780784482933.304
Longo, G.; Montrone, T.; Poloni, C. 2020. A new multi-objective solution approach using modeFRONTIER and OpenTrack for energy-efficient train timetabling problem, Computational Methods in Applied Sciences 54: 103–119. https://doi.org/10.1007/978-3-030-37752-6_7
Majumder, S.; Singh, Aa.; Singh, An.; Karpenko, M.; Sharma, H. K.; Mukhopadhyay, S. 2024. On the analytical study of the service quality of Indian railways under soft-computing paradigm, Transport 39(1): 54–63. https://doi.org/10.3846/transport.2024.21385
Meng, L.; Muneeb Abid, M.; Jiang, X.; Khattak, A.; Babar Khan, M. 2019. Increasing robustness by reallocating the margins in the timetable, Journal of Advanced Transportation 2019: 1382394. https://doi.org/10.1155/2019/1382394
Middelkoop, D.; Steneker, J.; Meijer, S.; Sehic, E.; Mazzarello, M. 2012. Simulation backbone for gaming simulation in railways: a case study, in Proceedings of the 2012 Winter Simulation Conference (WSC), 9–12 December 2012, Berlin, Germany, 1–13. https://doi.org/10.1109/WSC.2012.6465195
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Pouryousef, H.; Lautala, P. 2015. Hybrid simulation approach for improving railway capacity and train schedules, Journal of Rail Transport Planning & Management 5(4): 211–224. https://doi.org/10.1016/j.jrtpm.2015.10.001
Salido, M. A.; Barber, F.; Ingolotti, L. 2008. Robustness in railway transportation scheduling, in 2008 7th World Congress on Intelligent Control and Automation, 25–27 June 2008, Chongqing, China, 2880–2885. https://doi.org/10.1109/WCICA.2008.4594481
Schöbel, A.; Schreiner, H.; Baltram, S. 2022. Design of railway infrastructure with OpenTrack in Austria, in CETRA 2022: 7th International Conference on Road and Rail Infrastructure, 11–13 May 2022, Pula, Croatia, 787–792. https://doi.org/10.5592/CO/CETRA.2022.1347
Sels, P.; Dewilde, T.; Cattrysse, D.; Vansteenwegen, P. 2016. Reducing the passenger travel time in practice by the automated construction of a robust railway timetable, Transportation Research Part B: Methodological 84: 124–156. https://doi.org/10.1016/j.trb.2015.12.007
Solinen, E.; Palmqvist, C.-W. 2023. Development of new railway timetabling rules for increased robustness, Transport Policy 133: 198–208. https://doi.org/10.1016/j.tranpol.2023.02.003
Szűcs, G. 2001. Railway simulation with the CASSANDRA simulation system, Journal of Computing and Information Technology 9(2): 133–142. https://doi.org/10.2498/cit.2001.02.04
UTK. 2024. Punktualność pociągów pasażerskich w 2023 r. Urzędu Transportu Kolejowego (UTK), Warszawa, Polska. 40 s. Available from Internet: https://dane.utk.gov.pl/sts/analizy-i-opracowania/21214,Punktualnosc-pociagow-pasazerskich-w-2023-r.html (in Polish).
Wang, C.; Xia, Y.; Zhu, L. 2022. A method for identifying the important node in multi-layer logistic networks, Frontiers in Physics 10: 968645. https://doi.org/10.3389/fphy.2022.968645
Xu, X.-M.; Li, K.-P.; Yang, L.-X. 2014. Discrete event model-based simulation for train movement on a single-line railway, Chinese Physics B 23(8): 080205. https://doi.org/10.1088/1674-1056/23/8/080205
Yan, F.; Goverde, R. M. P. 2017. Railway timetable optimization considering robustness and overtakings, in 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 26–28 June 2017, Naples, Italy, 291–296. https://doi.org/10.1109/MTITS.2017.8005683
Zhu, Q. 2023. Passenger flow simulation of Xiamafang metro station based on AnyLogic, Highlights in Science, Engineering and Technology 37: 142–156. https://doi.org/10.54097/hset.v37i.6069
Alessandretti, L.; Natera Orozco, L. G.; Saberi, M.; Szell, M.; Battiston, F. 2023. Multimodal urban mobility and multilayer transport networks, Environment and Planning B: Urban Analytics and City Science 50(8): 2038–2070. https://doi.org/10.1177/23998083221108190
Andersson, E. V. 2014. Assessment of Robustness in Railway Traffic Timetables. Linköping Studies in Science and Technology. Licentiate Thesis No 1636. Linköping University, Norrköping, Sweden. 107 p. https://doi.org/10.3384/lic.diva-103676
Aredah, A. S.; Fadhloun, K.; Rakha, H. A. 2024. NeTrainSim: a network-level simulator for modeling freight train longitudinal motion and energy consumption, Railway Engineering Science 32(4): 480–498. https://doi.org/10.1007/s40534-024-00331-x
Bigdeli, A.; Tizghadam, A.; Leon-Garcia, A. 2009. Comparison of network criticality, algebraic connectivity, and other graph metrics, in SIMPLEX’09: Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners, 1 July 2009, Venice, Italy, 1–6. https://doi.org/10.1145/1610304.1610308
Chen, Z.; Han, B. M. 2014. Simulation study based on OpenTrack on carrying capacity in district of Beijing–Shanghai high-speed railway, Applied Mechanics and Materials 505–506: 567–570. https://doi.org/10.4028/www.scientific.net/AMM.505-506.567
Cole, C.; Spiryagin, M.; Wu, Q.; Sun, Y. Q. 2017. Modelling, simulation and applications of longitudinal train dynamics, Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility 55(10): 1498–1571. https://doi.org/10.1080/00423114.2017.1330484
D’Ariano, A. 2010. Improving real-time train dispatching performance: optimization models and algorithms for re-timing, re-ordering and local re-routing, 4OR 8(4): 429–432. https://doi.org/10.1007/s10288-010-0131-y
Deng, J.; Meng, Y.; Fu, Y.; Du, Y.; Guo, H. 2023. Research on the simulation of emergency evacuation of rail transit stations based on Anylogic, in 2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC), 15–17 September 2023, Chongqing, China, 1163–1167. https://doi.org/10.1109/ITOEC57671.2023.10291978
Dewilde, T.; Sels, P.; Cattrysse, D.; Vansteenwegen, P. 2011. Defining robustness of a railway timetable, in Proceedings of 4th International Seminar on Railway Operations Modelling and Analysis (IAROR): RailRome2011, 16–18 February 2011, Rome, Italy, 1–20.
Du, W.-B.; Zhou, X.-L.; Jusup, M.; Wang, Z. 2016. Physics of transportation: towards optimal capacity using the multilayer network framework, Scientific Reports 6: 19059. https://doi.org/10.1038/srep19059
Dudakova, A. V.; Goncharova, N. Y.; Kazakov, A. L.; Bolshakov, R. S. 2023. The modeling of technological processes of a railway marshalling yard using any logic, AIP Conference Proceedings 2624(1): 040049. https://doi.org/10.1063/5.0132718
Fischetti, M.; Salvagnin, D.; Zanette, A. 2009. Fast approaches to improve the robustness of a railway timetable, Transportation Science 43(3): 321–335. https://doi.org/10.1287/trsc.1090.0264
Goerigk, M.; Schmidt, M.; Schöbel, A.; Knoth, M.; Müller-Hannemann, M. 2014. The price of strict and light robustness in timetable information, Transportation Science 48(2): 225–242. https://doi.org/10.1287/trsc.2013.0470
Harrod, S.; Cerreto, F.; Nielsen, O. A. 2019. A closed form railway line delay propagation model, Transportation Research Part C: Emerging Technologies 102: 189–209. https://doi.org/10.1016/j.trc.2019.02.022
Jeremić, D.; Milosavljević, M.; Vujović, D. 2021. Simulation model of Belgrade suburban passenger trains service using OpenTrack, in Proceedings – Third International Conference “Transport for Today’s Society”, 14–16 October 2021, Bitola, Republic of North Macedonia, 108–111. https://doi.org/10.20544/TTS2021.1.1.21.p26
Jin, B.; Feng, X.; Wang, Q.; Wang, X.; Liu, C. 2019. Improving timetable robustness through optimal distribution of runtime supplement, in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 27–30 October 2019, 2803–2808. https://doi.org/10.1109/ITSC.2019.8917477
Karpenko, M.; Prentkovskis, O. 2022. Review of researches tendency in the future of the road transport, in Transport Means 2022. Sustainability: Research and Solutions: Proceedings of the 26th International Scientific Conference, 5–7 October 2022, Kaunas, Lithuania, 2: 724–728.
Khoshniyat, F.; Peterson, A. 2017. Improving train service reliability by applying an effective timetable robustness strategy, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 21(6): 525–543. https://doi.org/10.1080/15472450.2017.1326114
Kierzkowski, A.; Kisiel, T. 2015a. Functional readiness of the check-in desk system at an airport, Advances in Intelligent Systems and Computing 365: 223–233. https://doi.org/10.1007/978-3-319-19216-1_21
Kierzkowski, A.; Kisiel, T. 2015b. Modelling the passenger flow at an airport terminal to increase the safety level, in International Conference on Military Technologies (ICMT) 2015, 19–21 May 2015, Brno, Czechia, 1–7. https://doi.org/10.1109/MILTECHS.2015.7153693
Kierzkowski, A.; Kisiel, T. 2015c. Simulation model of logistic support for functioning of ground handling agent, taking into account a random time of aircrafts arrival, in International Conference on Military Technologies (ICMT) 2015, 19–21 May 2015, Brno, Czechia, 1–6. https://doi.org/10.1109/MILTECHS.2015.7153694
Li, K.; Gao, Z. 2007. An improved equation model for the train movement, Simulation Modelling Practice and Theory 15(9): 1156–1162. https://doi.org/10.1016/j.simpat.2007.07.006
Liang, Y. J.; Yuan, Z. Z. 2014. Assignment performance at Beijing south railway station based on Anylogic simulation, Advanced Materials Research 989–994: 2283–2287. https://doi.org/10.4028/www.scientific.net/AMR.989-994.2283
Liu, L.; Chen, H. 2020. Pedestrian emergency evacuation strategy in subway station based on AnyLogic, in CICTP 2020: Transportation Evolution Impacting Future Mobility, 14–16 August 2020, Xi’an, China, 3524–3533. https://doi.org/10.1061/9780784482933.304
Longo, G.; Montrone, T.; Poloni, C. 2020. A new multi-objective solution approach using modeFRONTIER and OpenTrack for energy-efficient train timetabling problem, Computational Methods in Applied Sciences 54: 103–119. https://doi.org/10.1007/978-3-030-37752-6_7
Majumder, S.; Singh, Aa.; Singh, An.; Karpenko, M.; Sharma, H. K.; Mukhopadhyay, S. 2024. On the analytical study of the service quality of Indian railways under soft-computing paradigm, Transport 39(1): 54–63. https://doi.org/10.3846/transport.2024.21385
Meng, L.; Muneeb Abid, M.; Jiang, X.; Khattak, A.; Babar Khan, M. 2019. Increasing robustness by reallocating the margins in the timetable, Journal of Advanced Transportation 2019: 1382394. https://doi.org/10.1155/2019/1382394
Middelkoop, D.; Steneker, J.; Meijer, S.; Sehic, E.; Mazzarello, M. 2012. Simulation backbone for gaming simulation in railways: a case study, in Proceedings of the 2012 Winter Simulation Conference (WSC), 9–12 December 2012, Berlin, Germany, 1–13. https://doi.org/10.1109/WSC.2012.6465195
Mongkoldee, K.; Leeton, U.; Kulworawanichpong, T. 2016. Single train movement modelling and simulation with rail potential consideration, in 2016 IEEE/SICE International Symposium on System Integration (SII), 13–15 December 2016, Sapporo, Japan, 7–12. https://doi.org/10.1109/SII.2016.7843967
PKP. 2017. Wykaz maksymalnych prędkości – pociągi pasażerskie. PKP Polskie Linie Kolejowe S. A., Warszawa, Polska. 74 s. Available from Internet: https://www.plk-sa.pl/files/public/user_upload/pdf/Reg_przydzielania_tras/Regulamin_2016_2017/07.04.2016/N_ZAL_2.1P_20160407092753.pdf (in Polish).
Pouryousef, H.; Lautala, P. 2015. Hybrid simulation approach for improving railway capacity and train schedules, Journal of Rail Transport Planning & Management 5(4): 211–224. https://doi.org/10.1016/j.jrtpm.2015.10.001
Salido, M. A.; Barber, F.; Ingolotti, L. 2008. Robustness in railway transportation scheduling, in 2008 7th World Congress on Intelligent Control and Automation, 25–27 June 2008, Chongqing, China, 2880–2885. https://doi.org/10.1109/WCICA.2008.4594481
Schöbel, A.; Schreiner, H.; Baltram, S. 2022. Design of railway infrastructure with OpenTrack in Austria, in CETRA 2022: 7th International Conference on Road and Rail Infrastructure, 11–13 May 2022, Pula, Croatia, 787–792. https://doi.org/10.5592/CO/CETRA.2022.1347
Sels, P.; Dewilde, T.; Cattrysse, D.; Vansteenwegen, P. 2016. Reducing the passenger travel time in practice by the automated construction of a robust railway timetable, Transportation Research Part B: Methodological 84: 124–156. https://doi.org/10.1016/j.trb.2015.12.007
Solinen, E.; Palmqvist, C.-W. 2023. Development of new railway timetabling rules for increased robustness, Transport Policy 133: 198–208. https://doi.org/10.1016/j.tranpol.2023.02.003
Szűcs, G. 2001. Railway simulation with the CASSANDRA simulation system, Journal of Computing and Information Technology 9(2): 133–142. https://doi.org/10.2498/cit.2001.02.04
UTK. 2024. Punktualność pociągów pasażerskich w 2023 r. Urzędu Transportu Kolejowego (UTK), Warszawa, Polska. 40 s. Available from Internet: https://dane.utk.gov.pl/sts/analizy-i-opracowania/21214,Punktualnosc-pociagow-pasazerskich-w-2023-r.html (in Polish).
Wang, C.; Xia, Y.; Zhu, L. 2022. A method for identifying the important node in multi-layer logistic networks, Frontiers in Physics 10: 968645. https://doi.org/10.3389/fphy.2022.968645
Xu, X.-M.; Li, K.-P.; Yang, L.-X. 2014. Discrete event model-based simulation for train movement on a single-line railway, Chinese Physics B 23(8): 080205. https://doi.org/10.1088/1674-1056/23/8/080205
Yan, F.; Goverde, R. M. P. 2017. Railway timetable optimization considering robustness and overtakings, in 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 26–28 June 2017, Naples, Italy, 291–296. https://doi.org/10.1109/MTITS.2017.8005683
Zhu, Q. 2023. Passenger flow simulation of Xiamafang metro station based on AnyLogic, Highlights in Science, Engineering and Technology 37: 142–156. https://doi.org/10.54097/hset.v37i.6069