A. Placido | C. Petito | M. Gallo | L. D'Acierno
OPEN ACCESS
In the case of conventional rail lines, when disruptions occur, dispatchers have the difficult task of finding feasible rescheduling solutions rapidly so as to re-establish ordinary conditions as soon as possible. Despite the numerous contributions for automatic rescheduling proposed in the literature, this process is still totally controlled by dispatchers who decide according to their personal experience and under their own responsibility. Indeed, in many cases, it can be more advantageous to let the system revert to ordinary conditions without implementing any strategy rather than look for solutions which can reduce the discomfort perceived by passengers. In this article we propose a system of models for managing the rail system, combining a microscopic simulation model with an assignment tool which is able to consider passenger flows on the network. as a result, the disutility experienced by users during their trip can be evaluated and feasible intervention strategies can be assessed, taking into account the passengers’ perspective. an application on a real regional line in campania (Italy) shows the benefits of the proposed approach for performing off-line analyses of intervention solutions and helping dispatchers make decisions during critical events to increase service quality.
public transport management, rail network micro-simulation, real-scale network analysis, travel demand estimation
[1] 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 Research Part B, 63, pp. 15–37, 2014. DOI: 10.1016/j.trb.2014.01.009.
[2] D'Ariano, A. & Albrecht, T., Running time re-optimization during real-time timetableperturbations. WIT Transactions on the Built Environment, 88, pp. 531–540, 2006.DOI: 10.2495/CR060531.
[3] D'Ariano, A., Pranzo, M. & Hansen, I.A., Conflict resolution and train speed coordinationfor solving real-time timetable perturbations. IEEE Transactions on IntelligentTransportation Systems, 8(2), pp. 208–222, 2007. DOI: 10.1109/TITS.2006.888605.
[4] D'Ariano, A., Improving Real-Time Train Dispatching: Models, Algorithms andApplications.PhD thesis, Delft University of Technology, The Netherlands, 2008.
[5] Goverde, R.M.P., Railway timetable stability analysis using max-plus system theory.Transportation Research Part B, 41(2), pp. 179–201, 2007. DOI: 10.1016/j.trb.2006.02.003.
[6] Goverde, R.M.P., A delay propagation algorithm for large-scale railway traffic networks.Transportation Research Part C, 18(3), pp. 269–287, 2010. DOI: 10.1016/j.trc.2010.01.002.
[7] Corman, F., D'Ariano, A., Pacciarelli, D. & Pranzo, M., Evaluation of a green wavepolicy in real-time railway traffic management. Transportation Research Part C, 17(6),pp. 607–616, 2009. DOI: 10.1016/j.trc.2009.04.001.
[8] Corman, F., D'Ariano, A., Pacciarelli, D. & Pranzo, M., A tabu search algorithm forreroutingtrains during rail operations. Transportation Research Part B, 44(1), pp. 175–192,2010. DOI: 10.1016/j.trb.2009.05.004.
[9] Corman, F., D'Ariano, A., Pranzo, M. & Hansen, I.A., Effectiveness of dynamic reorderingand rerouting of trains in a complicated and densely occupied station area. TransportationPlanning and Technology, 34(4), pp. 341–362, 2011. DOI: 10.1080/03081060.2011.577152.
[10] D'Ariano, A., Pacciarelli, D. & Pranzo, M., A branch and bound algorithm for schedulingtrains on a railway network. European Journal of Operational Research, 183(2),pp. 643–657, 2007. DOI: 10.1016/j.ejor.2006.10.034.
[11] D'Ariano, A., Corman, F., Pacciarelli, D. & Pranzo, M., Reordering and local reroutingstrategies to manage train traffic in real time. Transportation Science, 42(4), pp. 405–419,2008. DOI: 10.1287/trsc.1080.0247.
[12] M ascis, A. & Pacciarelli, D., Job-shop scheduling with blocking and no-wait constraints.European Journal of Operational Research, 143(3), pp. 498–517, 2002. DOI:10.1016/S0377-2217(01)00338-1.
[13] Quaglietta, E. Corman, F. & Goverde, R.M.P., Impact of a stochastic and dynamic settingon the stability of railway dispatching solutions. Proceedings of the 14th IEEEConference on Intelligent Transportation Systems (ITSC), pp. 1035–1040, 2013.
[14] Canca, D., Zarzo, A., Algaba, E. & Barrena, E., Confrontation of different objectives inthe determination of train scheduling. Procedia – Social and Behavioral Sciences, 20,pp. 302–312, 2011. DOI: 10.1016/j.sbspro.2011.08.036.
[15] Canca, D., Barrena, E., Zarzo, A., Ortega, F. & Algaba, E., Optimal train reallocationstrategies under service disruptions. Procedia – Social and Behavioral Sciences, 54,pp. 402–413, 2012. DOI: 10.1016/j.sbspro.2012.09.759.
[16] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interactionbetweentravel demand and rail capacity constraints. WIT Transactions on the BuiltEnvironment, 128, pp. 197–207, 2012. DOI: 10.2495/UT120181.
[17] Hamdouch, Y., Ho, H.W., Sumalee, A. & Wang, G., Schedule-based transit assignmentmodel with vehicle capacity and seat availability. Transportation Research Part B,45(10), pp. 1805–1830, 2011. DOI: 10.1016/j.trb.2011.07.010.
[18] Kanai, S., Shiina, K., Harada, S. & Tomii, N., An optimal delay management algorithmfrom passengers' viewpoints considering the whole railway network. Journalof Rail Transport Planning & Management, 1(1), pp. 25–37, 2011. DOI: 10.1016/j.jrtpm.2011.09.003.
[19] Zheng, Y., Zhang, Z., Xu, B. & Wang, L., Carrying capacity reliability of railway networks.Journal of Transportation Systems Engineering and Information Technology,11(4), pp. 16–21, 2011. DOI: 10.1016/S1570-6672(10)60128-6.
[20] Bifulco, G.N., Cantarella, G.E., De Luca, S. & Di Pace, R., Analysis and modellingthe effects of information accuracy on travellers' behaviour. Proceedings of the 14thIEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC,pp. 2098–2105, 2011.
[21] Dziekan, K. & Kottenhoff, K., Dynamic at-stop real-time information displays for publictransport: effects on customers. Transportation Research Part A, 41(6), pp. 489–501,2007. DOI: 10.1016/j.tra.2006.11.006.
[22] Molina, E.J.E. & Timmermans, H.J.P., Traveler expectations and willingness-to-pay forweb-enabled public transport information services. Transportation Research Part C,14(2), pp. 57–67, 2006. DOI: 10.1016/j.trc.2006.05.003.
[23] Paulley, N., Balcombe, R., Mackett, R., Titheridge, H., Preston, J. Wardman, M.,Shires, J. & White, P., The demand for public transport: The effects of fares, qualityof service, income and car ownership. Transport Policy, 13(4), pp. 295–306, 2006.DOI: 10.1016/j.tranpol.2005.12.004.
[24] Hansen, I.A., & Pachl. J., Railway Timetable and Traffic: Analysis, Modelling, Simulation,Eurail Press: Hamburg, Germany, 2008.
[25] Nash, A. & Huerlimann, D., Railroad simulation using OpenTrack. Computers in Railways,9, pp. 45–54, 2004. DOI: 10.2495/CR040051.
[26] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model frameworkfor managing rail systems in the case of breakdowns. Proceedings of the 16th IEEEConference on Intelligent Transportation Systems (ITSC), The Hague, pp. 1059–1064,2013.
[27] MVA Consultancy, Understanding the Passenger: Valuation of Overcrowding on RailServices. Report for Department of Transport, London, 2008.
[28] CENELEC, Railway Applications – Specification and Demonstration of Reliability,Availability, Maintainability and Safety (RAMS). EN50126, 1999.
[29] Pachl, J., Railway Operation and Control. VTD Rail Publishing: Mountlake Terrace,WA, 2009.
[30] Cascetta, E., Transportation Systems Analysis: Models and Applications. Springer:New York, 2009.
[31] Wardman, M. & Whelan, G., Twenty years of rail crowding valuation studies: Evidenceand lessons from British experience. Transport Reviews, 31(3), pp. 379–398, 2011.DOI: 10.1080/01441647.2010.519127.