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Transport policy appraisal with equilibrium models I: Generated traffic and highway investment benefits

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  • Williams, H. C. W. L.
  • Lam, W. M.

Abstract

In a series of three papers we investigate within an equilibrium framework the magnitude and composition of benefits arising from the various responses of travellers and operators to transport investments. In particular, we determine both analytically in simplified models and numerically in network representations, the extent to which the benefits to generated traffic may be more than off-set by the additional congestion inflicted on other road users. In this paper we adopt a uni-modal network model to appraise a variety of highway schemes and explore the sensitivity of user benefits to: the demand model specification; the inherent elasticity parameters; the level of congestion in a reference network; and the policies selected. The implications of the results for the methods of investment appraisal in urban and nonurban contexts are considered.

Suggested Citation

  • Williams, H. C. W. L. & Lam, W. M., 1991. "Transport policy appraisal with equilibrium models I: Generated traffic and highway investment benefits," Transportation Research Part B: Methodological, Elsevier, vol. 25(5), pages 253-279, October.
  • Handle: RePEc:eee:transb:v:25:y:1991:i:5:p:253-279
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    Cited by:

    1. Kidokoro, Yukihiro, 2006. "Benefit estimation of transport projects--a representative consumer approach," Transportation Research Part B: Methodological, Elsevier, vol. 40(7), pages 521-542, August.
    2. Justin Siegel & Joaquín Cea & José Fernández & Renán Rodriguez & David Boyce, 2006. "Comparisons of Urban Travel Forecasts Prepared with the Sequential Procedure and a Combined Model," Networks and Spatial Economics, Springer, vol. 6(2), pages 135-148, June.
    3. Huw C W L Williams & Dirk Van Vliet & Kwang Sik Kim, 2001. "The Contribution of Suppressed and Induced Traffic in Highway Appraisal, Part 1: Reference States," Environment and Planning A, , vol. 33(6), pages 1057-1082, June.

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