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Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Author

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  • Thanapong Champahom

    (Department of Management, Faculty of Business Administration, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand)

  • Sajjakaj Jomnonkwao

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Chinnakrit Banyong

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

  • Watanya Nambulee

    (Division of Logistic, Engineering Faculty of Engineering, Nakhon Phanom University, Nakhon Phanom 48000, Thailand)

  • Ampol Karoonsoontawong

    (Department of Civil Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand)

  • Vatanavongs Ratanavaraha

    (School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand)

Abstract

Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

Suggested Citation

  • Thanapong Champahom & Sajjakaj Jomnonkwao & Chinnakrit Banyong & Watanya Nambulee & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2021. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10086-:d:631922
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    References listed on IDEAS

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    Cited by:

    1. Thanapong Champahom & Chamroeun Se & Sajjakaj Jomnonkwao & Tassana Boonyoo & Vatanavongs Ratanavaraha, 2023. "A Comparison of Contributing Factors between Young and Old Riders of Motorcycle Crash Severity on Local Roads," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    2. Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    3. Thanapong Champahom & Chamroeun Se & Sajjakaj Jomnonkwao & Tassana Boonyoo & Amphaphorn Leelamanothum & Vatanavongs Ratanavaraha, 2023. "Temporal Instability of Motorcycle Crash Fatalities on Local Roadways: A Random Parameters Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 20(5), pages 1-28, February.
    4. Muhammad Wisal Khattak & Hans De Backer & Pieter De Winne & Tom Brijs & Ali Pirdavani, 2024. "Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models," Sustainability, MDPI, vol. 16(4), pages 1-22, February.

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