Development of the method for modeling the propagation of delays in non­cyclic train scheduling on the railroads with mixed traffic

Authors

DOI:

https://doi.org/10.15587/1729-4061.2018.123141

Keywords:

railroad network, section, train, non-cyclic train scheduling, delay, mixed traffic, multiagent optimization

Abstract

The main goal of present study is to develop a method for modeling delay propagation in non-cyclic train scheduling on a railroad network with mixed traffic. This will make it possible to explore the dynamics of delay transfer between trains and to identify the most vulnerable points in the timetable of trains. We have devised a method for modeling delay propagation in non-cyclic train scheduling for the rail networks with mixed traffic. It is proposed to apply as a basis of the developed method a mathematical model for the construction of a non-cyclic train timetable. A distinctive feature of the objective function of the mathematical model is taking into consideration the patterns of building a non-cyclic train timetable under conditions of mixed traffic of passenger and heavy-weight or multi-car freight trains, for which it is important to minimize the cost of stopping during motion. The proposed mathematical model was solved based on the multiagent optimization. To account for delay propagation on the railroad network of great dimensionality, we devised a procedure for connecting interdependent sections, which makes it possible to decompose the general problem based on the construction of schedule of trains for separate estimated sections taking into consideration the network effect. We performed an analysis of the dynamics of propagation of secondary delays in non-cyclic train scheduling with detailed patterns of changes in all parameters in time and space. We obtained dependences of the number and duration of delayed trains on the point of occurrence in the timetable of trains along the estimated line of the Ukrainian railroad network. The approach proposed allows the automatization of determining a time reserve in the standard non-cyclic train scheduling based on forecasting the consequences of train delays.

Author Biographies

Tatyana Butko, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Professor

Department of management of operational work

Andrii Prokhorchenko, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

Doctor of Technical Sciences, Associate Professor

Department of management of operational work

Tetiana Golovko, Ukrainian State University of Railway Transport Feierbakh sq., 7, Kharkiv, Ukraine, 61050

PhD, Associate Professor

Department of management of operational work

Galyna Prokhorchenko, Ukrainian State University of Railway Transport Feuerbach sq., 7, Kharkiv, Ukraine, 61001

Аssistant

Department of management of operational work

References

  1. Lomotko, D. V., Alyoshinsky, E. S., Zambrybor, G. G. (2016). Methodological Aspect of the Logistics Technologies Formation in Reforming Processes on the Railways. Transportation Research Procedia, 14, 2762–2766. doi: 10.1016/j.trpro.2016.05.482
  2. Tkachenko, V., Sapronova, S., Kulbovskiy, I., Fomin, O. (2017). Research into resistance to the motion of railroad undercarriages related to directing the wheelsets by a rail track. Eastern-European Journal of Enterprise Technologies, 5 (7 (89)), 65–72. doi: 10.15587/1729-4061.2017.109791
  3. Balanov, V. O. (2015). Analiz faktorov, vliyayushchih na obespechenie dvizheniya gruzovyh poezdov po raspisaniyu. Transportni systemy ta tekhnolohiyi perevezen, 10, 5–9.
  4. Zharkov, M. L., Parsyurova, P. A., Kazakov, A. L. (2014). Modelirovanie raboty stanciy i uchastkov zheleznodorozhnoy seti na osnove izucheniya otkloneniy ot grafika dvizheniya. Vestnik Irkutskogo gosudarstvennogo universiteta, 6 (89), 23–31.
  5. Büker, T., Seybold, B. (2012). Stochastic modelling of delay propagation in large networks. Journal of Rail Transport Planning & Management, 2 (1-2), 34–50. doi: 10.1016/j.jrtpm.2012.10.001
  6. Yan, F., Goverde, R. M. P. (2015). Stability and Robustness Analysis of Acyclic Timetable. 6th International Conference on Railway Operations Modelling and Analysis. Narashimo, 391–350.
  7. Kliewer, N., Suhl, L. (2010). A note on the online nature of the railway delay management problem. Networks, 57 (1), 28–37. doi: 10.1002/net.20381
  8. Schwanhäusser, W. (1974). Die Besessung der Pufferzeiten im Fahrplangefüge der Eisenbahn. Veröffentlichungen verkehrswissenschaftl. Institut RWTH Aachen, H. 20.
  9. Hansen, I. A. (2004). Increase of capacity through optimised timetabling. Computers in Railways IX, 529–538.
  10. Kecman, P., Corman, F., Meng, L. (2015). Train delay evolution as a stochastic process. Proceedings of the 6th International Conference on Railway Operations Modelling and Analysis: RailTokyo2015, 19.
  11. Yuan, J., Goverde, R. M. P., Hansen, I. A. (2010). Evaluating stochastic train process time distribution models on the basis of empirical detection data. WIT Transactions on State of the Art in Science and Engineering, 95–104. doi: 10.2495/978-1-84564-500-7/09
  12. Schwanhäußer, W. (1994). DVWG (Hrsg.): Schriftenreihe der Deutschen Verkehrswissenschaftlichen Gesellschaft Leistungsfahigkeit und Kapazitat. DVWG Reihe B: Seminar Bd. 17, 3–17.
  13. Vromans, M. J. C. M. (2005). Reliability of Railway Systems. Erasmus University Rotterdam, 258.
  14. Sipilä, H. (2015). Simulation of rail traffic – Methods for timetable construction, delay modeling and infrastructure evaluation. Stockholm, 98.
  15. Kettner, M., Sewcyk, B., Eickmann, C. (2003). Integrating Microscopic and Macroscopic Models for Railway Network Evaluation. In Proceedings of European Transport Conference. Strasbourg, France, 11.
  16. Goverde, R. M. P. (2010). A delay propagation algorithm for large-scale railway traffic networks. Transportation Research Part C: Emerging Technologies, 18 (3), 269–287. doi: 10.1016/j.trc.2010.01.002
  17. Nash, A., Huerlimann, D. (2004). Railroad simulation using OpenTrack. Computers in Railways IX, 45–54.
  18. Viriato – Software for railways (2012). SMA und Partner AG, 34. Available at: http://www.railsystemsengineering.com/RSE/documents/SMA%20Viriato%20Brochure.pdf
  19. Umiliacchi, S. (2016). Improving railway operations through the integration of macroscopic and microscopic modelling with optimisation. UK, 128.
  20. Farkas, M., Héray, T., Rózsa, G., Kóczy, T. L. (2009). Delay Propagation in a Real Life Railway Network Controlled by a Fuzzy Logic Rule Base. Magyar Kutatók 10. Ne mzetközi Szimpóziuma 10th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, 423–433.
  21. Daganzo, C. F., Laval, J. A. (2003). Moving Bottlenecks: A Numerical Method that Converges in Flows. Institute of Transportation Studies and Department of Civil and Environmental Engineering University of California. Berkeley, 56.
  22. Long, J., Gao, Z., Orenstein, P., Ren, H. (2012). Control Strategies for Dispersing Incident-Based Traffic Jams in Two-Way Grid Networks. IEEE Transactions on Intelligent Transportation Systems, 13 (2), 469–481. doi: 10.1109/tits.2011.2171035
  23. Wang, W. H., Zhang, W., Guo, H. W., Bubb, H., Ikeuchi, K. (2011). A behavioural car-following safety model for microscopic simulation of traffic flow with various driving characteristics. Transportation Research Part C: Emerging Technologies, 19 (6), 1202–1214. doi: 10.1016/j.trc.2011.02.002
  24. Dai, X., Hu, M., Tian, W., Xie, D., Hu, B. (2016). Application of Epidemiology Model on Complex Networks in Propagation Dynamics of Airspace Congestion. PLOS ONE, 11 (6), e0157945. doi: 10.1371/journal.pone.0157945
  25. Nyström, В. (2008). Aspects of Improving Punctuality From Data to Decision in Railway Maintenance. Sweden, 276.
  26. UIC leaflet 406 R, Capacity. UIC International Union of Railways, France, 2e édition (2013). Version traduite. List of recent publications, 60.
  27. TsD-0040. Instruktsiya zi skladannia hrafika rukhu poizdiv na zaliznytsiakh Ukrainy (2002). Kyiv: Transport Ukrainy, 164.
  28. Abril, M., Barber, F., Ingolotti, L., Salido, M. A., Tormos, P., Lova, A. (2008). An assessment of railway capacity. Transportation Research Part E: Logistics and Transportation Review, 44 (5), 774–806. doi: 10.1016/j.tre.2007.04.001
  29. Schachtebeck, M. (2010). Delay management in public transportation: capacities, robustness, and integration. Universität Göttingen, 240.
  30. Landex, A. (2007). Network effects in railway systems. Association for European Transport and contributors, 12, 16–20.
  31. Garey, M. R., Johnson, D. S. (1979). Computers and Intractability: a Guide to the Theory of NP-Completeness. Freeman, San Francisco, 215.
  32. Morton, T., Pentico, D. W. (1993). Heuristic Scheduling Systems, with Applications to Production Systems and Project Management. New York, 153.
  33. Butko, T. V., Prokhorchenko, H. O. (2015). Formuvannia protsedury avtomatyzatsiyi rozrobky hrafiku rukhu poizdiv na osnovi alhorytmu shtuchnykh bdzholynykh koloniyi. Zbirnyk naukovykh prats Dnipropetrovskoho natsionalnoho universytetu zaliznychnoho transportu imeni akademika V. Lazariana. Transportni systemy ta tekhnolohiyi perevezen, 9, 10–15.
  34. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N. (2012). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42 (1), 21–57. doi: 10.1007/s10462-012-9328-0
  35. Kozachenko, D. N., Berezoviy, N. I., Balanov, V. O., Zhuravel', V. V. (2015). Rezervy vremeni pri organizacii dvizheniya gruzovyh poezdov po raspisaniyu. Nauka ta prohres transportu. Visnyk Dnipropetrovskoho natsionalnoho universytetu zaliznychnoho transportu imeni akademika V. Lazariana, 2, 105–115.

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Published

2018-02-08

How to Cite

Butko, T., Prokhorchenko, A., Golovko, T., & Prokhorchenko, G. (2018). Development of the method for modeling the propagation of delays in non­cyclic train scheduling on the railroads with mixed traffic. Eastern-European Journal of Enterprise Technologies, 1(3 (91), 30–39. https://doi.org/10.15587/1729-4061.2018.123141

Issue

Vol. 1 No. 3 (91) (2018): Control processes

Section

Control processes

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Copyright (c) 2018 Tatyana Butko, Andrii Prokhorchenko, Tetiana Golovko, Galyna Prokhorchenko

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