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Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency

Author

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  • Sai Chand

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Zhuolin Li

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

  • Abdulmajeed Alsultan

    (Department of Civil Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Vinayak V. Dixit

    (Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency.

Suggested Citation

  • Sai Chand & Zhuolin Li & Abdulmajeed Alsultan & Vinayak V. Dixit, 2022. "Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5726-:d:810885
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    References listed on IDEAS

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    Cited by:

    1. Qiang Shang & Tian Xie & Yang Yu, 2022. "Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data," IJERPH, MDPI, vol. 19(17), pages 1-19, September.
    2. Huiping Li & Yunxuan Li, 2023. "A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data," Mathematics, MDPI, vol. 11(13), pages 1-24, June.

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