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A link based network route choice model with unrestricted choice set

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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://www.transportportal.se/swopec/CTS2013-10.pdf
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Bibliographic Info

Paper provided by CTS - Centre for Transport Studies Stockholm (KTH and VTI) in its series Working papers in Transport Economics with number 2013:10.

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Length: 40 pages
Date of creation: 16 Sep 2013
Date of revision:
Handle: RePEc:hhs:ctswps:2013_010

Note: Full bibliographic details: Transportation Research Part B: Methodological, Vol. 56, Oct. 2013, pp 70–80 DOI information: 10.1016/j.trb.2013.07.012
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Postal: Centrum för Transportstudier (CTS), Teknikringen 10, 100 44 Stockholm, Sweden
Web page: http://www.cts.kth.se/

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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. Fosgerau, Mogens, 2006. "Investigating the distribution of the value of travel time savings," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 688-707, September.
  2. 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.
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  6. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
  7. DeSerpa, A C, 1971. "A Theory of the Economics of Time," Economic Journal, Royal Economic Society, vol. 81(324), pages 828-46, December.
  8. Fosgerau, Mogens & McFadden, Daniel & Bierlaire, Michel, 2010. "Choice probability generating functions," MPRA Paper 24214, University Library of Munich, Germany.
  9. 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.
  10. 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.
  11. Melo, Emerson, 2012. "A representative consumer theorem for discrete choice models in networked markets," Economics Letters, Elsevier, vol. 117(3), pages 862-865.
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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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