A Simulation-Based Approach for Estimating Railway Capacity

A Simulation-Based Approach for Estimating Railway Capacity

Luca D’Acierno Marilisa Botte Giuseppe Pignatiello

Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Italy

Page: 
232-244
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DOI: 
https://doi.org/10.2495/TDI-V3-N3-232-244
Received: 
N/A
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Revised: 
N/A
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Accepted: 
N/A
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Available online: 
N/A
| Citation

OPEN ACCESS

Abstract: 

The article proposes a simulation-based approach for supporting a threshold analysis aimed at identifying the maximum number of trains to be operated on a line, given the related infrastructural and operational constraints. The method addresses an intermediate case between the theoretical and practical capacity conditions (i.e. simulated capacity). Moreover, the evaluated capacity represents an up- per-bound value and, therefore, it is independent of the involved demand flows which, hence, have been neglected in the provided discussion. In particular, against an initial effort for building the rail micro-simulation model, which requires the modelling of infrastructure layout, signalling system, roll- ing stock and planned timetable, the presented methodology allows infrastructure managers to properly direct the decision-making process by providing information on the effects of any intervention, in ad- vance of its effective implementation. In order to show the feasibility and usefulness of the proposed approach, it has been applied in the case of a real rail network context in the south of Italy.

Keywords: 

Railway systems, rail simulation models, railway capacity estimation, threshold analysis, timetabling design process

  References

[1] Goverde, R.M.P., Punctuality of railway operations and timetable stability analysis. Ph.D. dissertation, Delft University of Technology, The Netherlands, 2005.

[2] Corman, F., D’Ariano, A. & Hansen, I.A., Disruption handling in large railway networks. WIT Transactions on The Built Environment, 114, pp. 629–640, 2010.

[3] Cadarso, L., Marín, Á. & Maróti, G., Recovery of disruptions in rapid transit networks. Transportation Research Part E, 53, pp. 15–33, 2013.

[4] Binder, S., Maknoon, Y. & Bierlaire, M., Passenger-oriented railway disposition timetables in case of severe disruptions. Proceedings of the 15th Swiss Transport Research Conference (STRC 2015), Ascona, Switzerland, 2015.

[5] Botte, M. & D’Acierno, L., Dispatching and rescheduling tasks and their interactions with travel demand and the energy domain: Models and algorithms. Urban Rail Transit, 4(4), pp. 163–197, 2018.

[6] Kepaptsoglou, K. & Karlaftis, M.G., A model for analyzing metro station platform conditions following a service disruption. Proceedings of the13th International IEEE Annual Conference on Intelligent Transportation Systems (IEEE ITSC 2010), Funchal, Portugal, pp. 1789–1794, 2010.

[7] Cascetta, E., Cartenì, A. & Henke, I., Stations quality, aesthetics and attractiveness of rail transport: empirical evidence and mathematical models. Ingegneria Ferroviaria, 69(4), pp. 307–324, 2014.

[8] Di Mauro, R., Botte, M. & D’Acierno, L., An analytical methodology for extending passenger counts in a metro system. International Journal of Transport Development and Integration, 1(3), pp. 589–600, 2017.

[9] Xu, W., Zhao, P. & Ning, L., A passenger-oriented model for train rescheduling on an urban rail transit line considering train capacity constraint. Mathematical Problems in Engineering, 2017, article no. 1010745, pp. 1–9, 2017.

[10] Zhu, Y. & Goverde R.M.P., Dynamic passenger assignment during disruptions in railway systems. Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (IEEE MT-ITS 2017), Naples, Italy, pp. 146–151, 2017.

[11] D’Acierno, L., Botte, M. & Montella, B., Assumptions and simulation of passenger behaviour on rail platforms. International Journal of Transport Development and Integration, 2(2), pp. 123–135, 2018.

[12] Gallo, M., Improving equity of urban transit systems with the adoption of origin-destination based taxi fares. Socio-Economic Planning Sciences, 64, pp. 38–55, 2018.

[13] Kim, K.M., Kim, K.T. & Han, M.S., A model and approaches for synchronized energy saving in timetabling. Proceedings of 9th World Congress on Railway Research (WCRR 2011), Lille, France, 2011.

[14] Chevrier, R., Pellegrini, P. & Rodriguez, J., Energy saving in railway timetabling: A bi-objective evolutionary approach for computing alternative running times. Transportation Research Part C, 37, pp. 20–41, 2013.

[15] D’Acierno, L., Botte, M., Gallo, M. & Montella, B., Defining reserve times for metro systems: An analytical approach. Journal of Advanced Transportation, 2018, art. no. 5983250, pp. 1–15, 2018.

[16] D’Acierno, L. & Botte, M., Passengers’ satisfaction in the case of energy-saving strategies: A rail system application. Proceedings of the 18th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC 2018) and 2nd Industrial and Commercial Power Systems Europe (I&CPS 2018), Palermo, Italy, pp. 795–799, 2018.

[17] D’Acierno, L. & Botte, M., A passenger-oriented optimization model for implementing energy-saving strategies in railway contexts. Energies, 11(11), art. no. 2946, pp. 1–25, 2018.

[18] Cartenì, A., Accessibility indicators for freight transport terminals. Arabian Journal for Science and Engineering, 39(11), pp. 7647–7660, 2014.

[19] Cartenì, A., Urban sustainable mobility. Part 1: Rationality in transport planning. Transport Problems, 9(4), pp. 39–48, 2014.

[20] Cartenì, A., Urban sustainable mobility. Part 2: Simulation models and impacts estimation. Transport Problems, 10(1), pp. 5–16, 2015.

[21] Gallo, M., The impact of urban transit systems on property values: A model and some evidences from the city of Naples. Journal of Advanced Transportation, 2018, art. no. 1767149, pp. 1–22, 2018.

[22] 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.

[23] Guglielminetti, P., Piccioni, C., Fusco, G., Licciardello, R. & Musso, A., Single wagonload traffic in Europe: Challenges, prospects and policy options. Ingegneria Ferroviaria, 70(11), pp. 927–948, 2015.

[24] D’Acierno, L., Botte, M., Placido, A., Caropreso, C. & Montella, B., Methodology for determining dwell times consistent with passenger flows in the case of metro services. Urban Rail Transit, 3(2), pp. 73–89, 2017.

[25] Miyatake, M. & Matsuda, K., Energy saving speed and charge/discharge control of a railway vehicle with on-board energy storage by means of an optimization model. IEEJ Transactions on Electrical and Electronic Engineering, 4(6), pp. 771–778, 2009.

[26] Albrecht, A., Howlett, P., Pudney, P. & Vu, X., Energy-efficient train control: from local convexity to global optimization and uniqueness. Automatica, 49(10), pp. 3072–3078, 2013.

[27] De Martinis, V., Weidmann, U. & Gallo, M., Towards a simulation-based framework for evaluating energy-efficient solutions in train operation, WIT Transactions on the Built Environment, 135, pp. 721–732, 2014.

[28] D’Acierno, L., Botte, M. & Montella, B., An analytical approach for determining reserve times on metro systems. Proceedings of the 17th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC 2017) and 1st Industrial and Commercial Power Systems Europe (I&CPS 2017), Milan, Italy, pp. 722–727, 2017.

[29] Cornic, D., Efficient recovery of braking energy through a reversible dc substation. Proceedings of Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS 2010), Bologna, Italy, 2010.

[30] Ibaiondo, H. & Romo, A., Kinetic energy recovery on railway systems with feedback to the grid. Proceedings of the 14th International Power Electronics and Motion Control Conference (EPE-PEMC 2010), Ohrid, Macedonia, pp. 94–97, 2010.

[31] Domínguez, M., Fernández-Cardador, A., Cucala, A.P. & Pecharromán, R.R., Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles. IEEE Transactions on Automation Science and Engineering, 9(3), pp. 496–504, 2012.

[32] Prencipe, F.P. & Petrelli, M., Analytical methods and simulation approaches for determining the capacity of the Rome-Florence “Direttissima” line. Ingegneria Ferroviaria, 73(7–8), pp. 599–633, 2018.

[33] International Union of Railways (UIC), UIC Code 406: Capacity. 2nd ed., 2013.

[34] Schwanhäusser, W., Die Bemessung der Pufferzeiten im Fahrplangefüge der Eisenbahn, Ph.D. Dissertation, RWTH Aachen University, Germany, 1974.

[35] Bonora, G. & Giuliani, L., I criteri di calcolo di potenzialità delle linee ferroviarie. Ingegneria Ferroviaria, 37(7), 1982.

[36] International Union of Railways (UIC), UIC Leaflet 405-1: Method to be used for the determination of the capacity of Lines, 1983.

[37] Rete Ferroviaria Italiana – RFI (Italian National Railway Infrastructure Manager), Metodi di calcolo della capacità delle linee ferroviarie, Technical Report, 2011.

[38] Schultze, K., Gast, I. & Schwanhäusser, W., Sls plus – Einführung, Koblenz, Berlin, Germany, 2015.

[39] Gonzalez, J., Rodriguez, C., Blanquer, J., Mera, J.M., Castellote, E. & Santos, R., Increase of metro line capacity by optimisation of track circuit length and location: In a distance to go system. Journal of Advanced Transportation, 44(2), pp. 53–71, 2010.

[40] Lindfeldt, A., Railway capacity analysis: Methods for simulation and evaluation of timetables, delays and infrastructure. Ph.D. Dissertation, KTH Royal Institute of Technology, Sweden, 2015.

[41] Middelkoop, D. & Bouwman, M., SIMONE: Large scale train network simulations. Proceedings of the 2001 Winter Simulation Conference, Piscataway (NJ), USA, pp. 1042–1047, 2001.

[42] Sewcyk, B. & Kettner, M., Network Evaluation Model NEMO. Proceedings of the 5th World Congress on Rail Research (WCRR 2001), Cologne, Germany, 2001.

[43] Marinov, M. & Viegas, J., A mesoscopic simulation modelling methodology for analyzing and evaluating freight train operations in a rail network. Simulation Modelling Practice and Theory, 19(1), pp. 516–539, 2011.

[44] De Fabris, S., Longo, G., Medeossi, G. & Pesenti, R., Automatic generation of railway timetables based on a mesoscopic infrastructure model. Journal of Rail Transport Planning & Management, 4(1-2), pp. 2–13, 2014.

[45] Radtke, A. & Bendfeldt, J., Handling of railway operation problems with RailSys. Proceedings of the 5th World Congress on Rail Research (WCRR 2001), Cologne, Germany, 2001.

[46] Quaglietta, E., A Microscopic Simulation Model for supporting the design of railway systems: development and applications. Ph.D. dissertation, University of Naples Federico II, Italy, 2011.

[47] Quaglietta, E., Punzo, V., Montella, B., Nardone, R. & Mazzocca, N., Towards a hybrid mesoscopic-microscopic railway simulation model. Proceedings of the 2nd IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (IEEE MT-ITS 2011), Leuven, Belgium, 2011.

[48] Botte, M., Di Salvo, C., Placido, A., Montella, B. & D’Acierno, L., A Neighbourhood Search Algorithm for determining optimal intervention strategies in the case of metro system failures. International Journal of Transport Development and Integration, 1(1), pp. 63–73, 2017.

[49] Quaglietta, E., Corman, F. & Goverde, R.M.P., Impact of a stochastic and dynamic setting on the stability of railway dispatching solutions. Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (IEEE ITSC 2013), The Hague, The Netherlands, pp. 1035–1040, 2013.

[50] Quaglietta, E. & Punzo, V., Supporting the design of railway systems by means of a Sobol variance-based sensitivity analysis. Transportation Research Part C, 34, pp. 38–54, 2013.

[51] D’Acierno, L., Placido, A., Botte, M., Gallo, M. & Montella, B. Defining robust recovery solutions for preserving service quality during rail/metro systems failure. International Journal of Supply and Operations Management, 3(3), pp. 1351–1372, 2016.

[52] D’Acierno, L., Placido, A., Botte, M. & Montella B., A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, 10, pp. 80–86, 2016.

[53] Jacobs, J. Reducing delays by means of computer-aided ‘on-the-spot’ rescheduling, WIT Transactions on The Built Environment, 74, pp. 603–612, 2004.

[54] Nash, A. & Huerlimann, D., Railroad simulation using OpenTrack. WIT Transactions on The Built Environment, 74, pp. 45–54, 2004.