Abstract
Passenger routing in railway systems has traditionally relied on fixed timetables, working under the assumption that actual performance always matches the planned schedules. The complex variable interplay found in railway networks, however, make it practically impossible for trains to systematically hold the designed timetables as delays are a common occurrence in these systems. A more sensible approach to passenger routing involves assessing the probability distributions that characterize the system and consider them in the routing recommendation. This paper describes one such approach using a simulation model working under both deterministic and stochastic conditions and describes the weak points of a deterministic routing strategy in a complex system.
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References
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT press (2009)
Tong, C., Wong, S.: A stochastic transit assignment model using a dynamic schedule-based network. Transp. Res. B Methodol. 33(2), 107–121 (2002)
Geisberger, R.: Advanced route planning in transportation networks. Ph.D. dissertation, Karlsruher Instituts für Technologie (2011)
Fu, Q., Liu, R., Hess, S.: A review on transit assignment modelling approaches to congested networks: a new perspective,” Procedia Soc. Behav. Sci. 54, 1145–1155 (2012)
Rückert, R., Lemnian, M., Blendinger, C., Rechner, S., Müller-Hannemann, M.: PANDA: a software tool for improved train dispatching with focus on passenger flows. Public Transp. 9(1–2), 307–324 (2017)
Liu, Y., Blandin, S., Samaranayake, S.: Stochastic on-time arrival problem in transit networks. Transp. Res. B Methodol. 119, 122–138 (2019)
Gündling, F., Weihe, K., Hopp, F.: Efficient monitoring of public transport journeys. In: International Symposium on Rail Transport Demand Management (RTDM 2018) (2018)
Chow, J.: Informed Urban Transport Systems: Classic and Emerging Mobility Methods Toward Smart Cities. Elsevier (2018)
Borndörfer, R., Klug, T., Lamorgese, L., Mannino, C., Reuther, M., Schlechte, T.: Handbook of Optimization in the Railway Industry, vol. 268. Springer, Cham (2018)
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Barbeito, G., Moll, M., Bein, W., Pickl, S. (2020). Deterministic and Stochastic Simulation: A Combined Approach to Passenger Routing in Railway Systems. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_80
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DOI: https://doi.org/10.1007/978-3-030-48439-2_80
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