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Does Hypercongestion Exist? New Evidence Suggests Not

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  • Michael L. Anderson
  • Lucas W. Davis

Abstract

Transportation engineers are taught that as demand for travel goes up, this decreases not only speed but also the capacity of the road system, a phenomenon known as hypercongestion. We revisit this idea. There is no question that road systems experience periods in which capacity falls. However, we point out that capacity is determined by both demand and supply. Road construction, lane closures, stalled vehicles, weather, and other supply shocks provide an alternative explanation for the empirical evidence on hypercongestion. Using data from the Caldecott Tunnel in Oakland, California, we show that a naive regression recovers the standard hypercongestion result in the literature. However, once we use instrumental variables to isolate the effect of travel demand this effect disappears and across specifications we find no evidence that capacity decreases during periods of high demand. This lack of evidence of hypercongestion calls into question long-standing conventional wisdom held by transportation engineers and implies that efficient “Pigouvian” congestion taxes should be substantially lower than implied by hypercongestion models.

Suggested Citation

  • Michael L. Anderson & Lucas W. Davis, 2018. "Does Hypercongestion Exist? New Evidence Suggests Not," NBER Working Papers 24469, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24469
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    References listed on IDEAS

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    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
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    4. Hall, Jonathan D., 2018. "Pareto improvements from Lexus Lanes: The effects of pricing a portion of the lanes on congested highways," Journal of Public Economics, Elsevier, vol. 158(C), pages 113-125.
    5. Chung, Koohong & Rudjanakanoknad, Jittichai & Cassidy, Michael J., 2007. "Relation between traffic density and capacity drop at three freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 82-95, January.
    6. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-479, June.
    7. Akbar, Prottoy & Duranton, Gilles, 2017. "Measuring the Cost of Congestion in Highly Congested City: Bogotá," Research Department working papers 1028, CAF Development Bank Of Latinamerica.
    8. Keeler, Theodore E & Small, Kenneth A, 1977. "Optimal Peak-Load Pricing, Investment, and Service Levels on Urban Expressways," Journal of Political Economy, University of Chicago Press, vol. 85(1), pages 1-25, February.
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    More about this item

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • 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

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