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Product Differentiation on Roads: Constrained Congestion Pricing with Heterogeneous Users

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  • VERHOEF, ERIK T
  • Small, Kenneth A

Abstract

We explore the properties of various types of public and private pricing on a congested road network, with heterogeneous users and allowing for elastic demand. Heterogeneity is represented by a continuum of values of time. The network allows us to model certain features of real-world significance: pricing restrictions on either complementary or substitute links, as well as interactions between different user groups on shared links (e.g. in city centers). We find that revenue-maximizing pricing, whether restricted or unrestricted; but this difference is mitigated by the product differentiation made possible with heterogeneous users. Product differentiation also produces some unexpected distributional effects: those hurt most by pricing may be people with moderate rather than low values of time, and first-best pricing can cause congestion levels to increase for some users compared to no pricing. Ignoring heterogeneity causes the welfare benefits of a policy close to one currently being used, namely second-best pricing of one of two parallel links, to be dramatically underestimated. Unlike first-best policies, second-best policies are in danger of losing much of their potential effectiveness if heterogeneity is ignored when setting toll levels.

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Bibliographic Info

Paper provided by University of California Transportation Center in its series University of California Transportation Center, Working Papers with number qt37n0q7c6.

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Date of creation: 01 Jan 2003
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Handle: RePEc:cdl:uctcwp:qt37n0q7c6

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Keywords: Social and Behavioral Sciences;

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Cited by:
  1. Barry Ubbels & Erik Verhoef, 2004. "Auctioning Concessions for Private Roads," Tinbergen Institute Discussion Papers 04-008/3, Tinbergen Institute.
  2. Hyman, G. & Mayhew, L., 2008. "Toll optimisation on river crossings serving large cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 28-47, January.

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