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Trip distribution model for regional railway services considering spatial effects between stations

Author

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  • Cordera, Rubén
  • Sañudo, Roberto
  • dell’Olio, Luigi
  • Ibeas, Ángel

Abstract

The railways are a priority transport mode for the European Union given their safety record and environmental sustainability. Therefore it is important to have quantitative models available which allow passenger demand for rail travel to be simulated for planning purposes and to evaluate different policies. The aim of this article is to specify and estimate trip distribution models between railway stations by considering the most influential demand variables. Two types of models were estimated: Poisson regression and gravity. The input data were the ticket sales and the prices between stations on a regional line in Cantabria (Spain) which were provided by the Spanish railway infrastructure administrator (ADIF – RAM). The models have also considered the possible existence of spatial effects between train stations. The results show that the models have a good fit to the available data, especially the gravity models constrained by origins and destinations. Furthermore, the gravity models which considered the existence of spatial effects between stations had a significantly better fit and provided a more realistic journey pattern in a future scenario than the Poisson models and the gravity models that did not consider these effects. The proposed models have therefore been shown to be good support tools for decision making in the field of railway planning.

Suggested Citation

  • Cordera, Rubén & Sañudo, Roberto & dell’Olio, Luigi & Ibeas, Ángel, 2018. "Trip distribution model for regional railway services considering spatial effects between stations," Transport Policy, Elsevier, vol. 67(C), pages 77-84.
  • Handle: RePEc:eee:trapol:v:67:y:2018:i:c:p:77-84
    DOI: 10.1016/j.tranpol.2018.01.016
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    References listed on IDEAS

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    1. W.F. Lythgoe & M. Wardman, 2002. "Demand for rail travel to and from airports," Transportation, Springer, vol. 29(2), pages 125-143, May.
    2. Jean-Claude Thill & Marim Kim, 2005. "Trip making, induced travel demand, and accessibility," Journal of Geographical Systems, Springer, vol. 7(2), pages 229-248, June.
    3. Ennio Cascetta, 2009. "Transportation Systems Analysis," Springer Optimization and Its Applications, Springer, number 978-0-387-75857-2, June.
    4. Wardman, Mark, 2006. "Demand for rail travel and the effects of external factors," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(3), pages 129-148, May.
    5. Ennio Cascetta & Francesca Pagliara & Andrea Papola, 2007. "Alternative approaches to trip distribution modelling: A retrospective review and suggestions for combining different approaches," Papers in Regional Science, Wiley Blackwell, vol. 86(4), pages 597-620, November.
    6. Winkelmann, Rainer & Zimmermann, Klaus F, 1995. "Recent Developments in Count Data Modelling: Theory and Application," Journal of Economic Surveys, Wiley Blackwell, vol. 9(1), pages 1-24, March.
    7. G. M. Hyman, 1969. "The Calibration of Trip Distribution Models," Environment and Planning A, , vol. 1(1), pages 105-112, June.
    8. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
    9. Louis Grange & Angel Ibeas & Felipe González, 2011. "A Hierarchical Gravity Model with Spatial Correlation: Mathematical Formulation and Parameter Estimation," Networks and Spatial Economics, Springer, vol. 11(3), pages 439-463, September.
    10. Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
    11. James Raymer, 2007. "The Estimation of International Migration Flows: A General Technique Focused on the Origin-Destination Association Structure," Environment and Planning A, , vol. 39(4), pages 985-995, April.
    12. Daniel A. Griffith, 2009. "Spatial Autocorrelation in Spatial Interaction," Advances in Spatial Science, in: Aura Reggiani & Peter Nijkamp (ed.), Complexity and Spatial Networks, chapter 0, pages 221-237, Springer.
    13. repec:rre:publsh:v:37:y:2007:i:1:p:28-38 is not listed on IDEAS
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