A Review of Real Time Railway Traffic Management During Disturbances

  • Wenhua Qu
  • Francesco Corman
  • Gabriel Lodewijks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9335)


This paper gives an over view of real time traffic management of the railway network in case of disturbances. After briefly introducing the problem of disturbance management and basic mathematical formulations, this paper overviews the existing literatures according to the typologies of traffic and levels of detail in the infrastructure models used for railway traffic network representation. A precise placement is made based on the effect of management decisions towards the various stakeholders. The application of these models in real life railway system is discussed based on the special constraints considered, the size of the railway network and the calculation time. Most railway disturbance management models are tested in an experiment setting at present, and if applied in practice they can be helpful to dispatchers to provide a higher quality service for all stakeholders involved.


Railway network Real time traffic management Disturbance management Train types 


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  1. 1.
    Acuna-Agost, R., Michelon, P., Feillet, D., Gueye, S.: A MIP-based local search method for the railway rescheduling problem. Networks 57(1), 69–86 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Albrecht, T., Binder, A., Gassel, C.: An overview on real-time speed control in rail-bound public transportation systems. In: Proceedings of the 2nd International Transportation Conference-Leuven, Belgium (2011)Google Scholar
  3. 3.
    Błażewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: Conventional and new solution techniques. Eur. J. Oper. Res. 93(1), 1–33 (1996)CrossRefzbMATHGoogle Scholar
  4. 4.
    Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J.: An Overview of Recovery Models and Algorithms for Real-time Railway Rescheduling. Transportation Res. Part B 63, 15–37 (2014)CrossRefGoogle Scholar
  5. 5.
    Caimi, G., Fuchsberger, M., Laumanns, M., Lüthi, M.: A model predictive control approach for discrete-time rescheduling in complex central railway station areas. Compt. & Operations Res. 39, 2578–2593 (2012)CrossRefzbMATHGoogle Scholar
  6. 6.
    Caprara, A., Fischetti, M., Toth, P.: Modeling and solving the train timetabling problem. Oper. Res. 50(5), 851–861 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Corman, F.: Bi-objective conflict detection and resolution in railway traffic management. Transportation Res. Part C 20, 79–94 (2012)CrossRefGoogle Scholar
  8. 8.
    Corman, F., D’Ariano, A., Hansen, I.A., Pacciarelli, D.: Optimal multi-class rescheduling of railway traffic. J. Rail Transport Planning & Management 1, 14–24 (2011)CrossRefGoogle Scholar
  9. 9.
    Corman, F., D’Ariano, A., Pacciarelli, D., Pranzo, M.: A Tabu search algorithm for rerouting trains during rail operations. Transportation Res. Part B 44(1), 175–192 (2009)CrossRefGoogle Scholar
  10. 10.
    Corman, F., D’Ariano, A., Pacciarelli, D., Samá, M.: Railway Traffic Reschedule in with Minimization of Passengers’ Discomfort. Proceedings of the MT, ITS (2015)Google Scholar
  11. 11.
    D’Ariano, A.: Improving real-time train dispatching: models, algorithms, and applications. TRAIL Thesis Series, T2008/6, The Netherlands (2008)Google Scholar
  12. 12.
    D’Ariano, A., Corman, F., Pacciarelli, D., Pranzo, M.: Reordering and local rerouting strategies to manage train traffic in real time. Transportation Science 42(4), 405–419 (2008)CrossRefGoogle Scholar
  13. 13.
    D’Ariano, A., Pacciarelli, D., Pranzo, M.: A branch and bound algorithm for scheduling trains in a railway network. European J. Operational Res. 183(2), 643–657 (2007)CrossRefzbMATHGoogle Scholar
  14. 14.
    D’Ariano, A., Pacciarelli, D., Pranzo, M.: Assessment of flexible timetables in real-time traffic management of a railway bottleneck. Transportation Res. Part C 16(2), 232–245 (2008)CrossRefGoogle Scholar
  15. 15.
    Dewilde, T.: Improving the robustness of a railway system in large and complex station areas. Doctoral thesis (2014)Google Scholar
  16. 16.
    Dollevoet, T., Huisman, D., Kroon, L., Schmidt, M., Schöbel, A.: Delay Management including Capacities of Stations. Transportation Science 49(2), 185–203 (2015)CrossRefGoogle Scholar
  17. 17.
    Dollevoet, T., Huisman, D., Schmidt, M., Schöbel, A.: Delay management with rerouting of passengers. Transportation Science 46(1), 74–89 (2012)CrossRefGoogle Scholar
  18. 18.
    Dündar, S., Şahin, I.: Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways. Transportation Res. Part C: Emerging Technologies 27, 1–15 (2013)CrossRefGoogle Scholar
  19. 19.
    Gély, L., Dessagne, G., Lerin, C.: Modelling train re-scheduling with optimization and operational research techniques: results and applications at SNCF. In: Proceedings of WCRR, Montréal (2006)Google Scholar
  20. 20.
    Ginkel, A., Schöbel, A.: To wait or not to wait? The criteria delay management problem in public transportation. Transportation Science 41(4), 527–538 (2007)CrossRefGoogle Scholar
  21. 21.
    Godwin, T., Gopalan, R., Narendran, T.T.: A heuristic for routing and scheduling freight trains in a passenger rail network. International J. Logistics Systems and Management 3(1), 101–133 (2007)CrossRefzbMATHGoogle Scholar
  22. 22.
    Jespersen-Groth, J., Potthoff, D., Clausen, J., Huisman, D., Kroon, L.G., Maroti, G., Nielsen, M.N.: Disruption Management in Passenger Railway Transportation, Informatics and Mathematical Modelling, Technical University of Denmark, Kgs. Lyngby, Denmark (2007)Google Scholar
  23. 23.
    Jong, G., Tseng, Y., Kouwenhoven, M., Verhoef, E., Bates, J.: The Value of Travel Time and Travel Time Reliability. Prepared for The Netherlands Ministry of Transport, Public Works and Water Management (2007)Google Scholar
  24. 24.
    Kanai, S., Shiina, K., Harada, S., Tomii, N.: An optimal delay management algorithm from passengers’ viewpoints considering the whole railway network. J. Rail Transport Planning & Management 1, 25–37 (2011)CrossRefGoogle Scholar
  25. 25.
    Kraay, D.R., Harke, P.T.: Real-time scheduling of freight railroads. US Transportation Res. Part B 29(3), 213–229 (1995)CrossRefGoogle Scholar
  26. 26.
    Kuo, A., Elise, M.H., Mahmassani, H.S.: Freight train scheduling with elastic demand. Transportation Res. Part E 46, 1057–1070 (2010)CrossRefGoogle Scholar
  27. 27.
    Liebchen, C., Schachtebeck, M., Schöbel, A., Stiller, S., Prigge. A.: Computing delay-resistant railway timetables. Technical report, ARRIVAL Report TR-0071, Georg-August Universität, Göttingen, Germany (2007)Google Scholar
  28. 28.
    Liebchen, C., Stiller, S.: Delay resistant timetabling. Technical Report 2006/24, Technische Universität Berlin, CASPT 2006 (2006)Google Scholar
  29. 29.
    Mascis, A., Pacciarelli, D.: Job shop scheduling with blocking and no-wait constraints. European Journal of Operational Res. 143(3), 498–517 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Oetting, A., Keck, A.-K.: Monetarization of delay valuation for freight. In: 6th International Conference on Railway Operations Modelling and Analysis - RailTokyo2015 (2015)Google Scholar
  31. 31.
    Rodriguez, J.: A constraint programming model for real-time train scheduling at junctions. Transportation Res. Part B 41, 231–245 (2007)CrossRefGoogle Scholar
  32. 32.
    Schöbel, A.: A model for the delay management based on mixed integer programming. Electronic Notes Thoret. Comput. Sci. 50(1), 1–10 (2001)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Schöbel, A.: Integer programming approaches for solving the delay management problem. In: Geraets, F., Kroon, L.G., Schoebel, A., Wagner, D., Zaroliagis, C.D. (eds.) Railway Optimization 2004. LNCS, vol. 4359, pp. 145–170. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  34. 34.
    Schöbel, A.: Capacity constraints in delay management. Public Transport 1(2), 135–154 (2009)CrossRefGoogle Scholar
  35. 35.
    Törnquist, J.: Design of an effective algorithm for fast response to the rescheduling of railway traffic during disturbances. Transportation Res. Part C 20, 62–78 (2012)CrossRefGoogle Scholar
  36. 36.
    Törnquist, J., Persson, J.A.: N-tracked railway traffic re-scheduling during disturbances. Transportation Res. Part B 41(3), 342–362 (2007)CrossRefGoogle Scholar
  37. 37.
    Van der Hurk, E., Kroon, L.G., Maroti, G., Bouman, P., Vervest, P.H.M.: Network reduction and dynamic forecasting of passenger flows for disruption management. In: Proceedings of Rail Copenhagen Conference, Denmark (2013)Google Scholar
  38. 38.
    Wegele, S., Schnieder, E.: Dispatching of train operations using genetic algorithms. In: Hansen, I.A., Dekking, F.M., Goverde, R.M.P., Heidergott, B., Meester, L.E. (eds.) Proceedings of Rail Delft Conference, The Netherlands (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wenhua Qu
    • 1
  • Francesco Corman
    • 1
  • Gabriel Lodewijks
    • 1
  1. 1.Department of Maritime & Transport TechnologyDelft University of TechnologyDelftThe Netherlands

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