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An exploratory hazard-based analysis of highway incident duration

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  • Nam, Doohee
  • Mannering, Fred

Abstract

The statistical analysis of highway incident duration has become an increasingly import research topic due to the impact that highway incidents (vehicle accidents and disablements) have on traffic congestion. In addition, there is a growing need to evaluate incident management programs that seek to reduce incident duration and incident-induced traffic congestion. We apply hazard-based duration models to statistically evaluate the time it takes detect/report, respond to, and clear incidents. Two-year data from Washington State's incident response team program were used to estimate the hazard models. The model estimation results show that a wide variety of factors significantly affect incident times (i.e. detection/reporting, response, and clearance times), and that different distributional assumptions for the hazard function are appropriate for the different incident times being considered. It was also found that the estimated coefficients were not stable between the two years of data used in model estimation. The findings of this paper provide an important demonstration of method and an empirical basis to assess incident management programs.

Suggested Citation

  • Nam, Doohee & Mannering, Fred, 2000. "An exploratory hazard-based analysis of highway incident duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(2), pages 85-102, February.
  • Handle: RePEc:eee:transa:v:34:y:2000:i:2:p:85-102
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Adler, Martin W. & Ommeren, Jos van & Rietveld, Piet, 2013. "Road congestion and incident duration," Economics of Transportation, Elsevier, vol. 2(4), pages 109-118.
    2. Ng, ManWo & Khattak, Asad & Talley, Wayne K., 2013. "Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 44-57.
    3. Fosgerau, Mogens & Lindsey, Robin, 2013. "Trip-timing decisions with traffic incidents," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 764-782.
    4. Meng, Lingyun & Zhou, Xuesong, 2011. "Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 1080-1102, August.
    5. Iragaël Joly, 2004. "Travel Time Budget – Decomposition of the Worldwide Mean," Post-Print halshs-00087433, HAL.
    6. Hainen, Alexander M. & Remias, Stephen M. & Bullock, Darcy M. & Mannering, Fred L., 2013. "A hazard-based analysis of airport security transit times," Journal of Air Transport Management, Elsevier, vol. 32(C), pages 32-38.
    7. Hall, Randolph, 2000. "Incident Dispatching, Clearance and Delay," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2pp689vn, Institute of Transportation Studies, UC Berkeley.
    8. Piet Rietveld, 2013. "Climate change adaptation and transport: a review," Chapters,in: Smart Transport Networks, chapter 3, pages 29-48 Edward Elgar Publishing.
    9. Hall, Randolph W., 2002. "Incident dispatching, clearance and delay," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(1), pages 1-16, January.
    10. Geroliminis, Nikolas & Karlaftis, Matthew G. & Skabardonis, Alexander, 2009. "A spatial queuing model for the emergency vehicle districting and location problem," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 798-811, August.
    11. Steenbruggen, John & Nijkamp, Peter & van der Vlist, Maarten, 2014. "Urban traffic incident management in a digital society," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 245-261.
    12. Hall, Randolph W., 2001. "Incident Management: Process Analysis and Improvement," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt1jf6j37t, Institute of Transportation Studies, UC Berkeley.
    13. Cynthia Chen & Jason Chen, 2009. "What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?," Transportation, Springer, vol. 36(1), pages 27-46, January.

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