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A link based network route choice model with unrestricted choice set

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  • Fosgerau, Mogens
  • Frejinger, Emma
  • Karlstrom, Anders

Abstract

This paper considers the path choice problem, formulating and discussing an econometric random utility model for the choice of path in a network with no restriction on the choice set. Starting from a dynamic specification of link choices we show that it is equivalent to a static model of the multinomial logit form but with infinitely many alternatives. The model can be consistently estimated and used for prediction in a computationally efficient way. Similarly to the path size logit model, we propose an attribute called link size that corrects utilities of overlapping paths but that is link additive. The model is applied to data recording path choices in a network with more than 3,000 nodes and 7,000 links.

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File URL: http://mpra.ub.uni-muenchen.de/48707/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 48707.

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Date of creation: Jul 2013
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Handle: RePEc:pra:mprapa:48707

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Keywords: discrete choice; recursive logit; networks; route choice; infinite choice set;

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References

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  1. Victor Aguirregabiria & Pedro Mira, 1999. "Swapping the Nested Fixed-Point Algorithm: a Class of Estimators for Discrete Markov Decision Models," Computing in Economics and Finance 1999 332, Society for Computational Economics.
  2. Fosgerau, Mogens & McFadden, Daniel & Bierlaire, Michel, 2010. "Choice probability generating functions," MPRA Paper 24214, University Library of Munich, Germany.
  3. Mogens Fosgerau, 2004. "Investigating the distribution of the value of travel time savings," Urban/Regional 0410005, EconWPA, revised 25 Nov 2004.
  4. Frejinger, E. & Bierlaire, M., 2007. "Capturing correlation with subnetworks in route choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 363-378, March.
  5. Melo, Emerson, 2012. "A representative consumer theorem for discrete choice models in networked markets," Economics Letters, Elsevier, vol. 117(3), pages 862-865.
  6. Guevara, C. Angelo & Ben-Akiva, Moshe E., 2013. "Sampling of alternatives in Multivariate Extreme Value (MEV) models," Transportation Research Part B: Methodological, Elsevier, vol. 48(C), pages 31-52.
  7. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
  8. DeSerpa, A C, 1971. "A Theory of the Economics of Time," Economic Journal, Royal Economic Society, vol. 81(324), pages 828-46, December.
  9. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
  10. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  11. David A. Hensher, 2001. "Measurement of the Valuation of Travel Time Savings," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 35(1), pages 71-98, January.
  12. Flötteröd, Gunnar & Bierlaire, Michel, 2013. "Metropolis–Hastings sampling of paths," Transportation Research Part B: Methodological, Elsevier, vol. 48(C), pages 53-66.
  13. Akamatsu, Takashi, 1996. "Cyclic flows, Markov process and stochastic traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 30(5), pages 369-386, October.
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Cited by:
  1. Kitthamkesorn, Songyot & Chen, Anthony, 2014. "Unconstrained weibit stochastic user equilibrium model with extensions," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 1-21.
  2. Adriaan Hendrik van der Weijde & Vincent A.C. van den Berg, 2013. "Stochastic User Equilibrium Traffic Assignment with Price-sensitive Demand: Do Methods matter (much)?," Tinbergen Institute Discussion Papers 13-209/VIII, Tinbergen Institute.

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