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Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand

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  • Kwon, Jaimyoung
  • Mauch, Michael
  • Varaiya, Pravin

Abstract

A method is presented to divide the total congestion delay in a freeway section into six components: the delay caused by incidents, special events, lane closures, and adverse weather; the potential reduction in delay at bottlenecks that ideal ramp metering can achieve; and the remaining delay, caused mainly by excess demand. The fully automated method involves two steps. First, the components of nonrecurrent congestion are estimated by statistical regression. Second, the method locates all bottlenecks and estimates the potential reduction in delay that ideal ramp metering can achieve. The method can be applied to any site with minimum calibration. It requires data about traffic volume and speed; the time and location of incidents, special events, and lane closures; and adverse weather. Applied to a 45-mi section of I-880 in the San Francisco Bay Area in California, the method reveals that incidents, special events, rain, potential reduction by ideal ramp metering, and excess demand respectively account for 13.3%, 4.5%, 1.6%, 33.2%, and 47.4%, respectively, of the total daily delay. The delay distribution of the various components is different between the morning and evening peak periods and between the two freeway directions. Quantifying the components of congestion at individual freeway sites is essential for developing effective congestion mitigation strategies.

Suggested Citation

  • Kwon, Jaimyoung & Mauch, Michael & Varaiya, Pravin, 2006. "Components of Congestion: Delay from Incidents, Special Events, Lane Closures, Weather, Potential Ramp Metering Gain, and Excess Demand," University of California Transportation Center, Working Papers qt31c9k717, University of California Transportation Center.
  • Handle: RePEc:cdl:uctcwp:qt31c9k717
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    Cited by:

    1. Farnaz Khaghani & Farrokh Jazizadeh, 2020. "mD-Resilience: A Multi-Dimensional Approach for Resilience-Based Performance Assessment in Urban Transportation," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    2. Wen, Tzai-Hung & Chin, Wei-Chien-Benny & Lai, Pei-Chun, 2017. "Understanding the topological characteristics and flow complexity of urban traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 166-177.
    3. Pedro Cesar Lopes Gerum & Andrew Reed Benton & Melike Baykal-Gürsoy, 2019. "Traffic density on corridors subject to incidents: models for long-term congestion management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 795-831, December.
    4. Toan, Trinh Dinh & Wong, Y.D., 2021. "Fuzzy logic-based methodology for quantification of traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    5. Louis de Grange & Rodrigo Troncoso & Nicolás Odeh & Felipe González, 2017. "Estimating the impact of incidents on urban controlled-access highways: an empirical analysis," Applied Economics, Taylor & Francis Journals, vol. 49(18), pages 1763-1773, April.
    6. Seeherman, Joshua & Anderson, Paul, 2017. "Measuring the influence of recurring sporting events on freeway characteristics," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt40k7x1xc, Institute of Transportation Studies, UC Berkeley.
    7. Hall, Jonathan D. & Savage, Ian, 2019. "Tolling roads to improve reliability," Journal of Urban Economics, Elsevier, vol. 113(C).

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    Keywords

    Social and Behavioral Sciences;

    Statistics

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