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Urban travel demand model with endogenous congestion

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  • Batarce, Marco
  • Ivaldi, Marc

Abstract

We formulate and estimate a structural model for travel demand in which users have heterogeneous preferences and make their transport decisions based on network congestion. A key component in the model is the infinite number of users in the network, all of whom have common knowledge about the distribution of preferences in the population. In this setting, the congestion level is endogenously determined in the equilibrium of a game with a continuum of players. For the estimate, we use the first-order conditions of the users’ utility maximization problem to derive the likelihood function. For inference, we apply a two-step, semi-parametric method. Using data from Santiago, Chile, we show that the estimated parameters confirm the effect of congestion on individuals’ preferences and that demand elasticities obtained by using our framework are consistent with results reported in the literature. We use the model to evaluate the effect on the welfare of increasing the cost of car trips and implementing a second-best fare schedule for bus transit. We also assess the welfare loss caused by congestion in Santiago.

Suggested Citation

  • Batarce, Marco & Ivaldi, Marc, 2014. "Urban travel demand model with endogenous congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 331-345.
  • Handle: RePEc:eee:transa:v:59:y:2014:i:c:p:331-345
    DOI: 10.1016/j.tra.2013.12.006
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    Cited by:

    1. Louis Grange & Felipe González & Ignacio Vargas & Rodrigo Troncoso, 2015. "A Logit Model With Endogenous Explanatory Variables and Network Externalities," Networks and Spatial Economics, Springer, vol. 15(1), pages 89-116, March.
    2. Batarce, Marco & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2016. "Valuing crowding in public transport: Implications for cost-benefit analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 358-378.

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    Keywords

    Travel demand; Congestion; Network effects;

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