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An empirical test of hypercongestion in highway bottlenecks

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  • Anderson, Michael L.
  • Davis, Lucas W.

Abstract

There is a widely-held view 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 in the context of highway bottlenecks. We propose an empirical test using an event study design to measure changes in highway capacity at the onset of queue formation. We apply this test to three highway bottlenecks in California for which detailed data on traffic flows and vehicles speeds are available. We find no evidence of a reduction in highway capacity at any of the three sites during periods of high demand. Across sites and specifications we have sufficient statistical power to rule out even small reductions in highway capacity. This lack of evidence of hypercongestion stands in sharp contrast to most previous studies and informs core models in urban and transportation economics.

Suggested Citation

  • Anderson, Michael L. & Davis, Lucas W., 2020. "An empirical test of hypercongestion in highway bottlenecks," Journal of Public Economics, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:pubeco:v:187:y:2020:i:c:s004727272030061x
    DOI: 10.1016/j.jpubeco.2020.104197
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    Cited by:

    1. Jinwon Kim & Jucheol Moon & Dongyun Yang, 2024. "Pigouvian Congestion Tolls and the Welfare Gain: Estimates for California Freeways," Working Papers 2402, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    2. Russo, Antonio & Adler, Martin W. & Liberini, Federica & van Ommeren, Jos N., 2021. "Welfare losses of road congestion: Evidence from Rome," Regional Science and Urban Economics, Elsevier, vol. 89(C).
    3. Durrmeyer, Isis & Martinez, Nicolas, 2022. "The Welfare Consequences of Urban Traffic Regulations," TSE Working Papers 22-1378, Toulouse School of Economics (TSE).
    4. Lehe, Lewis J. & Pandey, Ayush, 2024. "A bathtub model of transit congestion," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    5. Anupriya & Daniel J. Graham & Daniel Horcher & Prateek Bansal, 2021. "Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach," Papers 2104.02399, arXiv.org.

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    More about this item

    Keywords

    Hypercongestion; Traffic congestion; Capacity drop; Speed; Traffic flows;
    All these keywords.

    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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