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An Analysis of the Impact of Injury Severity on Incident Clearance Time on Urban Interstates Using a Bivariate Random-Parameter Probit Model

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  • M. Ashifur Rahman

    (Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
    Louisiana Transportation Research Center, 4101 Gourrier Ave, Baton Rouge, LA 70808, USA)

  • Milhan Moomen

    (Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
    Louisiana Transportation Research Center, 4101 Gourrier Ave, Baton Rouge, LA 70808, USA)

  • Waseem Akhtar Khan

    (Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA)

  • Julius Codjoe

    (Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
    Louisiana Transportation Research Center, 4101 Gourrier Ave, Baton Rouge, LA 70808, USA)

Abstract

Incident clearance time (ICT) is impacted by several factors, including crash injury severity. The strategy of most transportation agencies is to allocate more resources and respond promptly when injuries are reported. Such a strategy should result in faster clearance of incidents, given the resources used. However, injury crashes by nature require extra time to attend to and move crash victims while restoring the highway to its capacity. This usually leads to longer incident clearance duration, despite the higher amount of resources used. This finding has been confirmed by previous studies. The implication is that the relationship between ICT and injury severity is complex as well as correlated with the possible presence of unobserved heterogeneity. This study investigated the impact of injury severity on ICT on Louisiana’s urban interstates by adopting a random-parameter bivariate modeling framework that accounts for potential correlation between injury severity and ICT, while also investigating unobserved heterogeneity in the data. The results suggest that there is a correlation between injury severity and ICT. Importantly, it was found that injury severity does not impact ICT in only one way, as suggested by most previous studies. Also, some shared factors were found to impact both injury severity and ICT. These are young drivers, truck and bus crashes, and crashes that occur during daylight. The findings from this study can contribute to an improvement in safety on Louisiana’s interstates while furthering the state’s mobility goals.

Suggested Citation

  • M. Ashifur Rahman & Milhan Moomen & Waseem Akhtar Khan & Julius Codjoe, 2024. "An Analysis of the Impact of Injury Severity on Incident Clearance Time on Urban Interstates Using a Bivariate Random-Parameter Probit Model," Stats, MDPI, vol. 7(3), pages 1-12, August.
  • Handle: RePEc:gam:jstats:v:7:y:2024:i:3:p:52-874:d:1453528
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    References listed on IDEAS

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    1. William H. Greene & David A. Hensher, 2010. "Ordered Choices and Heterogeneity in Attribute Processing," Journal of Transport Economics and Policy, University of Bath, vol. 44(3), pages 331-364, September.
    2. Williams, A.F. & Carsten, O., 1989. "Driver age and crash involvement," American Journal of Public Health, American Public Health Association, vol. 79(3), pages 326-327.
    3. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291, Decembrie.
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