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Road pricing modeling for hyper-congestion

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  • Lo, Hong K.
  • Szeto, W.Y.

Abstract

Recently there has been a resurgence in the interest of road pricing. Most studies adopt the static modeling paradigm, typically using either separable monotone or backward-bending link travel time functions for the analysis. In this study, through the shockwave analysis, we show that separable backward-bending functions are not appropriate for modeling hyper-congestion and hence road pricing. In the absence of queue spillback, link travel time is a monotone increasing function of inflow. However, in the presence of queue spillback, we show that the static paradigm even with a monotone travel time function cannot adequately portray the congestion phenomenon. In some cases, the tolls determined by the static paradigm can be even detrimental, worsening rather than alleviating the congestion problem. In the end, to model congested networks properly, perhaps one has no other choices but to adopt a modeling paradigm that faithfully captures both the temporal as well as the spatial dimensions of traffic queuing.

Suggested Citation

  • Lo, Hong K. & Szeto, W.Y., 2005. "Road pricing modeling for hyper-congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 705-722.
    • Handle: RePEc:eee:transa:v:39:y:2005:i:7-9:p:705-722
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    References listed on IDEAS

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    Cited by:

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    3. Holgun-Veras, Jos & Cetin, Mecit, 2009. "Optimal tolls for multi-class traffic: Analytical formulations and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 445-467, May.
    4. Jiang, Yanqun & Wong, S.C. & Ho, H.W. & Zhang, Peng & Liu, Ruxun & Sumalee, Agachai, 2011. "A dynamic traffic assignment model for a continuum transportation system," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 343-363, February.
    5. Du, Jie & Wong, S.C. & Shu, Chi-Wang & Zhang, Mengping, 2015. "Reformulating the Hoogendoorn–Bovy predictive dynamic user-optimal model in continuum space with anisotropic condition," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 189-217.
    6. Vosough, Shaghayegh & de Palma, André & Lindsey, Robin, 2022. "Pricing vehicle emissions and congestion externalities using a dynamic traffic network simulator," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 1-24.
    7. Holguín-Veras, José, 2011. "Urban delivery industry response to cordon pricing, time-distance pricing, and carrier-receiver policies in competitive markets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 802-824, October.
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    9. Holgui­n-Veras, Jose & Cetin, Mecit & Xia, Shuwen, 2006. "A comparative analysis of US toll policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 852-871, December.
    10. Tsekeris, Theodore & Geroliminis, Nikolas, 2013. "City size, network structure and traffic congestion," Journal of Urban Economics, Elsevier, vol. 76(C), pages 1-14.
    11. Du, Jie & Wong, S.C. & Shu, Chi-Wang & Xiong, Tao & Zhang, Mengping & Choi, Keechoo, 2013. "Revisiting Jiang’s dynamic continuum model for urban cities," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 96-119.
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