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The relationship between driving volatility in time to collision and crash injury severity in a naturalistic driving environment

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  • Behram Wali
  • Asad Khattak
  • Thomas Karnowski

Abstract

As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash injury severity. By using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 671 crash events featuring around 0.2 million temporal samples of real world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. To explore the relationships between crash-injury severity outcomes and driving volatility, the volatility indices are then linked with individual crash events including information on crash severity, drivers' pre crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an indepth analysis is conducted using the aggregate as well as segmented (based on time to collision) real world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter logit models with heterogeneity in parameter means and variances are estimated. The empirical results offer important insights regarding how driving volatility in time to collision relates to crash severity outcomes. Overall, statistically significant positive correlations are found between the aggregate (as well as segmented) volatility measures and crash severity outcomes. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) in time to collision increases the likelihood of police reportable or most severe crash events... ...

Suggested Citation

  • Behram Wali & Asad Khattak & Thomas Karnowski, 2020. "The relationship between driving volatility in time to collision and crash injury severity in a naturalistic driving environment," Papers 2010.04719, arXiv.org.
    • Handle: RePEc:arx:papers:2010.04719
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    File URL: http://arxiv.org/pdf/2010.04719
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    2. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    3. Behram Wali & Asad Khattak, 2020. "Harnessing Ambient Sensing & Naturalistic Driving Systems to Understand Links Between Driving Volatility and Crash Propensity in School Zones: A generalized hierarchical mixed logit framework," Papers 2010.12017, arXiv.org.
    4. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    5. Xiong, Yingge & Mannering, Fred L., 2013. "The heterogeneous effects of guardian supervision on adolescent driver-injury severities: A finite-mixture random-parameters approach," Transportation Research Part B: Methodological, Elsevier, vol. 49(C), pages 39-54.
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    Cited by:

    1. Yao Wu & Yanyong Guo & Wei Yin, 2021. "Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China," Sustainability, MDPI, vol. 13(4), pages 1-11, February.
    2. Torkashvand, Mojtaba Bahrami & Aghayan, Iman & Qin, Xiao & Hadadi, Farhad, 2022. "An extended dynamic probabilistic risk approach based on a surrogate safety measure for rear-end collisions on two-lane roads," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Wali, Behram & Santi, Paolo & Ratti, Carlo, 2023. "Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership," Technological Forecasting and Social Change, Elsevier, vol. 195(C).

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