Advertisement

International Journal of Civil Engineering

, Volume 16, Issue 2, pp 219–228 | Cite as

Assessing Temporary Speed Restrictions and Associated Unavailability Costs in Railway Infrastructure

  • António Ramos AndradeEmail author
  • Paulo Fonseca Teixeira
Research Paper

Abstract

This paper analyses the occurrence of temporary speed restrictions in railway infrastructure associated with railway track geometry degradation. A negative binomial regression model is put forward to estimate the expected number of temporary speed restrictions, controlling for the main quality indicators of railway track geometry degradation and for the maintenance and renewal actions/decisions. The prediction of temporary speed restrictions provides a quantitative way to support the assessment of unavailability costs to railway users. A case study on the Lisbon–Oporto Portuguese line is explored, comparing three statistical models: the Poisson, the ‘over-dispersed’ Poisson and the proposed negative binomial regression. Main findings suggest that the main quality indicators for railway track geometry degradation are statistically significant variables, apart from the maintenance and renewal actions. Finally, a discussion on the impacts of the unavailability costs associated with temporary speed restrictions is also provided in a regulated railway context.

Keywords

Temporary speed restrictions Railway maintenance Statistical modelling Negative binomial regression Unavailability costs 

Notes

Acknowledgements

The authors would like to thank the support and collaboration of the Portuguese Railway Infrastructure Manager, REFER, E. P. E, and of the Portuguese Foundation for Science and Technology (FCT) through the project MODURAIL (PTDC/SEN-TRA/112975/2009) and MIT Portugal program through the PhD Grant (SFRH/BD/33785/2009).

Compliance with ethical standards

Funding

Portuguese Foundation for Science and Technology (FCT) and MIT Portugal. Portuguese Railway Infrastructure Manager (REFER).

References

  1. 1.
    Stenström C, Parida A, Galar D (2014) Performance indicators of railway infrastructure. Int J Railw Technol 1(3):1–18CrossRefGoogle Scholar
  2. 2.
    Veiseth M, Olsson N, Saetermo IAF (2007) Infrastructure’s influence on rail punctuality. WIT Trans Built Environ 96:481–490CrossRefGoogle Scholar
  3. 3.
    Vickerman R (2004) Maintenance incentives under different infrastructure regimes. Util Policy 12(4):315–322CrossRefGoogle Scholar
  4. 4.
    Olsson NO, Haugland H (2004) Influencing factors on train punctuality—results from some Norwegian studies. Transp Policy 11(4):387–397CrossRefGoogle Scholar
  5. 5.
    Luque R, Castro M (2016) Highway geometric design consistency: speed models and local or global assessment. Int J Civ Eng. doi: 10.1007/s40999-016-0025-2 Google Scholar
  6. 6.
    Hansen IA (2010) Railway Network timetabling and dynamic traffic management. Int J Civ Eng 8(1):19–32Google Scholar
  7. 7.
    Murali P, Dessouky M, Ordóñez F, Palmer K (2010) A delay estimation technique for single and double-track railroads. Transp Res Part E Logist Transp Rev 46(4):483–495CrossRefGoogle Scholar
  8. 8.
    Schlake B, Barkan C, Edwards J (2011) Train delay and economic impact of in-service failures of railroad rolling stock. Transp Res Record J Transp Res Board 2261:124–133CrossRefGoogle Scholar
  9. 9.
    Marinov M, Şahin İ, Ricci S, Vasic-Franklin G (2013) Railway operations, time-tabling and control. Res Transp Econ 41(1):59–75CrossRefGoogle Scholar
  10. 10.
    Gorman MF (2009) Statistical estimation of railroad congestion delay. Transp Res Part E Logist Transp Rev 45(3):446–456CrossRefGoogle Scholar
  11. 11.
    Vansteenwegen P, Van Oudheusden D (2007) Decreasing the passenger waiting time for an intercity rail network. Transp Res Part B Methodol 41(4):478–492CrossRefGoogle Scholar
  12. 12.
    Nielsen OA, Landex A, Frederiksen RD (2008) Passenger delay models for rail networks. Sched Based Model Transp Netw Theory Appl 46:27Google Scholar
  13. 13.
    Higgins A, Kozan E, Ferreira L (1995) Modelling delay risks associated with train schedules. Transp Plan Technol 19(2):89–108CrossRefGoogle Scholar
  14. 14.
    Andrade AR, Teixeira PF (2011) Biobjective optimization model for maintenance and renewal decisions related to rail track geometry. Transp Res Record J Transp Res Board 2261:163–170CrossRefGoogle Scholar
  15. 15.
    Andrade AR, Teixeira PF (2014) Unplanned-maintenance needs related to rail track geometry. Proceedings of the Institution of Civil Engineers-Transport 167(6):400–410CrossRefGoogle Scholar
  16. 16.
    EN 13848-5 (2008): Railway applications–Track–Track geometry quality—Part 5: Geometric quality levelsGoogle Scholar
  17. 17.
    Andrade AR, Teixeira PF (2012) A Bayesian model to assess rail track geometry degradation through its life-cycle. Res Transp Econ 36(1):1–8CrossRefGoogle Scholar
  18. 18.
    Andrade AR, Teixeira PF (2013) Hierarchical Bayesian modelling of rail track geometry degradation. Proc Inst Mech Eng Part F J Rail Rapid Transit 227(4):364–375CrossRefGoogle Scholar
  19. 19.
    Andrade AR, Teixeira PF (2015) Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models. Reliab Eng System Saf 142:169–183CrossRefGoogle Scholar
  20. 20.
    Andrade AR, Teixeira PF (2016) Exploring different alert limit strategies in the maintenance of railway track geometry. J Transp Eng. doi: 10.1061/(ASCE)TE.1943-5436.0000867 Google Scholar
  21. 21.
    Sadeghi J (2010) Field investigation on vibration behavior of railway track systems. International J Civ Eng 8(3):232–241Google Scholar
  22. 22.
    Dahlberg T (2010) Railway track stiffness variations—consequences and countermeasures. Int J Civ Eng 8(1):1–12MathSciNetGoogle Scholar
  23. 23.
    Ver Hoef JM, Boveng PL (2007) Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88(11):2766–2772CrossRefGoogle Scholar
  24. 24.
    Guikema SD, Coffelt JP (2009) Practical considerations in statistical modeling of count data for infrastructure systems. J Infrastruct Syst 15(3):172–178CrossRefGoogle Scholar
  25. 25.
    Madanat S, Ibrahim WHW (1995) Poisson regression models of infrastructure transition probabilities. J Transp Eng 121(3):267–272CrossRefGoogle Scholar
  26. 26.
    Liu H, Davidson RA, Rosowsky DV, Stedinger JR (2005) Negative binomial regression of electric power outages in hurricanes. J Infrastruct syst 11(4):258–267CrossRefGoogle Scholar
  27. 27.
    Evans AW (2013) The economics of railway safety. Res Transp Econ 43(1):137–147CrossRefGoogle Scholar
  28. 28.
    Noland RB (2013) From theory to practice in road safety policy: understanding risk versus mobility. Res Transp Econ 43(1):71–84CrossRefGoogle Scholar
  29. 29.
    Friebel G, Ivaldi M, Vibest C (2010) Railway (De)regulation: a European efficiency comparison. Economica 77:77–91CrossRefGoogle Scholar

Copyright information

© Iran University of Science and Technology 2016

Authors and Affiliations

  • António Ramos Andrade
    • 1
    Email author
  • Paulo Fonseca Teixeira
    • 2
  1. 1.Instituto Universitário de Lisboa (ISCTE-IUL)Business Research Unit (BRU-IUL)LisbonPortugal
  2. 2.CESUR, CErisInstituto Superior Técnico, Universidade de LisboaLisbonPortugal

Personalised recommendations