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Credit-based congestion pricing: A Dallas-Fort Worth application

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  • Gulipalli, Pradeep K.
  • Kockelman, Kara M.

Abstract

Under a credit-based congestion pricing policy, net revenues are distributed uniformly among qualifying travelers, to partially offset toll payments. This work predicts the traffic impacts, air-quality changes, welfare effects, and system implementation costs of such a policy, as applied to the Dallas-Fort Worth (DFW) region of Texas. Joint destination-mode choice models were estimated and applied. The status quo and two marginal cost pricing (MCP) scenarios were simulated for the short and long terms, with full feedback of trip costs and times. Monetarized logsum differences suggest that marginal cost pricing of congested freeways in this urban region, followed by travel credit distribution to all workers, is welfare improving for the great majority of such travelers. Moreover, high levels of recurring congestion (V/C ratios exceeding 1.5) are predicted to practically disappear.

Suggested Citation

  • Gulipalli, Pradeep K. & Kockelman, Kara M., 2008. "Credit-based congestion pricing: A Dallas-Fort Worth application," Transport Policy, Elsevier, vol. 15(1), pages 23-32, January.
    • Handle: RePEc:eee:trapol:v:15:y:2008:i:1:p:23-32
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    References listed on IDEAS

    as
    1. Ian W. H. Parry & Antonio Bento, 2001. "Revenue Recycling and the Welfare Effects of Road Pricing," Scandinavian Journal of Economics, Wiley Blackwell, vol. 103(4), pages 645-671, December.
    2. Kockelman, Kara M. & Kalmanje, Sukumar, 2005. "Credit-based congestion pricing: a policy proposal and the public's response," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 671-690.
    3. repec:cdl:uctcwp:qt32p9m3mm is not listed on IDEAS
    4. repec:bla:scandj:v:103:y:2001:i:4:p:645-71 is not listed on IDEAS
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    Cited by:

    1. Gang Cheng & Shuzhi Zhao & Di Huang, 2018. "Understanding the Effects of Improving Transportation on Pilgrim Travel Behavior: Evidence from the Lhasa, Tibet, China," Sustainability, MDPI, vol. 10(10), pages 1-13, October.
    2. Chi Xie & Xing Wu & Stephen Boyles, 2019. "Traffic equilibrium with a continuously distributed bound on travel weights: the rise of range anxiety and mental account," Annals of Operations Research, Springer, vol. 273(1), pages 279-310, February.
    3. de Palma, André & Lindsey, Robin, 2020. "Tradable permit schemes for congestible facilities with uncertain supply and demand," Economics of Transportation, Elsevier, vol. 21(C).
    4. Beheshtian, Arash & Richard Geddes, R. & Rouhani, Omid M. & Kockelman, Kara M. & Ockenfels, Axel & Cramton, Peter & Do, Wooseok, 2020. "Bringing the efficiency of electricity market mechanisms to multimodal mobility across congested transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 58-69.
    5. Prateek Bansal & Kara M. Kockelman, 2018. "Are we ready to embrace connected and self-driving vehicles? A case study of Texans," Transportation, Springer, vol. 45(2), pages 641-675, March.
    6. Kockelman, Kara M. & Lemp, Jason D., 2011. "Anticipating new-highway impacts: Opportunities for welfare analysis and credit-based congestion pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 825-838, October.
    7. Fan, Wenbo & Jiang, Xinguo, 2013. "Tradable mobility permits in roadway capacity allocation: Review and appraisal," Transport Policy, Elsevier, vol. 30(C), pages 132-142.
    8. Siyu Chen & Ravi Seshadri & Carlos Lima Azevedo & Arun P. Akkinepally & Renming Liu & Andrea Araldo & Yu Jiang & Moshe E. Ben-Akiva, 2021. "Market Design for Tradable Mobility Credits," Papers 2101.00669, arXiv.org, revised Sep 2022.

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