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A Design Model for Rapid Transit Networks Considering Rolling Stock’s Reliability and Redistribution of Services During Disruptions

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 366)

Abstract

This paper presents a model for designing a public transit network system combining the traditional approach of transport demand coverage in bimodal scenarios of operation with the recovery of possible disruptions due to limited reliability of the rolling stock. The model balances construction and operational costs with the benefits to the users for the optimization of their travel times. Two transportation modes have been considered, public and private transport and the proportion of the users choosing one mode or the other is assumed to obey to a bimodal logit choice model. While construction costs are a first stage decision, user travel costs and recovery action costs are scenario dependent. Two types of scenarios are taken into account: a) the scenarios of normal operation and b) disruption scenarios which are associated to a link's breakdown of the network. The disruptions in the links are assumed to follow a probability disruption model accordingly to the number of services that operate on them. The model can be used to analyze the influence of the rate of failures of the units on the reliability of the designed RTN. The proposed model can be considered as a two recourse stochastic programming model with a bi-level structure where the probabilities of failure are an implicit function of the number of services and the routing of the transit lines of the transport system. A heuristic solution method is examined for small to medium networks demonstrating the computational viability of the approach.

Keywords

Rapid transit network design disruption management recoverability system’s reliability 

Notes

Acknowledgments

This research was supported by project grants TRA2011-27791-C03-01/02 by the Spanish Ministerio de Economía y Competitividad.

References

  1. 1.
    Bruno G., Gendreau M., and Laporte G. (2002). A heuristic for the location of a rapid transit line. Computers and Operations Research, 29, 1-12.CrossRefzbMATHGoogle Scholar
  2. 2.
    Cacchiani V., Caprara A., Galli, L., Kroon, L., Maróti, G., and Toth, P. (2011). Railway Rolling Stock Planning: Robustness against large disruptions. Transportation Scie., 1-16.Google Scholar
  3. 3.
    Cadarso L. and Marín, A. (2012). Recoverable Robustness in Rapid Transit Network Design. 15th EWGT, Paris, 10-13 September 2012.Google Scholar
  4. 4.
    Cicerone, S., D'Angelo, G., Di Stefano, G., Frigioni, D., Navarra, A., Schachtebeck, M., Schöbel, A. (2009). Recoverable robustness in shunting and timetabling, R. Ahuja, R. Möhring, C. Zaroliagis, eds. Robust and On-Line Large Scale Optimization Lectures Notes in Computer Science., Vol 5868. Springer-Verlag, Berlin, 28-60.Google Scholar
  5. 5.
    Evans SP (1975) Derivation and analysis of some models for combining trip distribution and assignment. Transportation research 10 pp 37-57 1975Google Scholar
  6. 6.
    Laporte G., Marín Á., Mesa J.A. and Ortega F.A. (2011). Designing robust rapid transit networks with alternative routes. Journal of Advanced Transportation, 45, 5-65.CrossRefGoogle Scholar
  7. 7.
    López-Ramos F. (2014) Conjoint design of railway lines and frequency setting under semi-congested scenarios. PhD Thesis. Departament d'Estadística i Investigació Operativa. Universitat Politècnica de Catalunya.Google Scholar
  8. 8.
    Marín Á. (2007). An extension to rapid transit network design problem. TOP, 15, 231–241.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Dept Statistics and Operations ResearchBarcelonaTECH-UPCBarcelonaSpain
  2. 2.Dept. of Applied Mathematics and StatisticsUPMMadridSpain

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