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The Use of Road Infrastructure Data for Urban Transportation Planning: Issues and Opportunities


  • David Levinson


  • Feng Xie

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)


In order to maintain and improve road infrastructure in their respective jurisdictions, the state (the Minnesota DOT), region (the Metropolitan Council), and seven counties in the Twin Cities Metropolitan area develop their respective decision making (investment) processes in which federal or local funding are periodically allocated to road projects, prioritized according to their funding needs based on measured road infrastructure conditions such as pavement quality, level of service, and safety. Including such an investment process in urban transportation planning enables forecasting changes to road infrastructure in the future. Periodic road infrastructure reporting provides standards the jurisdictions maintain, as well as the measures they adopt for the management of road conditions. These measures, developed and maintained by engineers, however, are inconsistent with transportation planning models, causing difficulties in fully using infrastructure reports in planning practice. This paper addresses the issues we encountered with regard to the use of road infrastructure reports in planning practice and identifies the opportunity to improve the inter-operability and integration of infrastructure reporting with urban transportation planning.

Suggested Citation

  • David Levinson & Feng Xie, 2007. "The Use of Road Infrastructure Data for Urban Transportation Planning: Issues and Opportunities," Working Papers 200711, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:roadinfrastructuredata.pdf

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    File Function: First version, 2007
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    References listed on IDEAS

    1. Cantillo, Víctor & Heydecker, Benjamin & de Dios Ortúzar, Juan, 2006. "A discrete choice model incorporating thresholds for perception in attribute values," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 807-825, November.
    2. Carlos Carrion & David Levinson, 2012. "Route choice dynamics after a link restoration," Working Papers 000105, University of Minnesota: Nexus Research Group.
    3. Samuelson, William & Zeckhauser, Richard, 1988. "Status Quo Bias in Decision Making," Journal of Risk and Uncertainty, Springer, vol. 1(1), pages 7-59, March.
    4. Mahmassani, Hani S. & Jou, Rong-Chang, 2000. "Transferring insights into commuter behavior dynamics from laboratory experiments to field surveys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(4), pages 243-260, May.
    5. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
    6. Guo, Xiaolei & Liu, Henry X., 2011. "Bounded rationality and irreversible network change," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1606-1618.
    7. Shlomo Bekhor & Moshe Ben-Akiva & M. Ramming, 2006. "Evaluation of choice set generation algorithms for route choice models," Annals of Operations Research, Springer, vol. 144(1), pages 235-247, April.
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    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • R53 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Public Facility Location Analysis; Public Investment and Capital Stock

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