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Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021

Author

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  • Khondoker Billah

    (School of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Hatim O. Sharif

    (Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Samer Dessouky

    (Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

Due to the number of severe traffic collisions involving motorcycles, a comprehensive investigation is required to determine their causes. This study analyzed Texas crash data from 2017 to 2021 to determine who was at fault and how various factors affect the frequency and severity of motorcycle collisions. Moreover, the study tried to identify high-risk sites for motorcycle crashes. Utilizing bivariate analysis and logistic regression models, the study investigated the individual and combined effects of several variables. Heat maps and hotspot analyses were used to identify locations with a high incidence of both minor and severe motorcycle crashes. The survey showed that dangerous speed, inattention, lane departure, and failing to surrender the right-of-way at a stop sign or during a left turn were the leading causes of motorcycle crashes. When a motorcyclist was at fault, the likelihood of severe collisions was much higher. The study revealed numerous elements as strong predictors of catastrophic motorcycle crashes, including higher speed limits, poor illumination, darkness during the weekend, dividers or designated lanes as the principal road traffic control, an increased age of the primary crash victim, and the lack of a helmet. The concentration of motorcycle collisions was found to be relatively high in city cores, whereas clusters of severe motorcycle collisions were detected on road segments beyond city limits. This study recommends implementing reduced speed limits on high-risk segments, mandating helmet use, prioritizing resource allocation to high-risk locations, launching educational campaigns to promote safer driving practices and the use of protective gear, and inspecting existing conditions as well as the road geometry of high-risk locations to reduce the incidence and severity of motorcycle crashes.

Suggested Citation

  • Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2023. "Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021," Sustainability, MDPI, vol. 15(13), pages 1-26, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10377-:d:1184258
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    References listed on IDEAS

    as
    1. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2021. "Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas," IJERPH, MDPI, vol. 18(17), pages 1-19, September.
    2. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2022. "How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    3. Heng-Yu Lin & Jian-Sing Li & Chih-Wei Pai & Wu-Chien Chien & Wen-Cheng Huang & Chin-Wang Hsu & Chia-Chieh Wu & Shih-Hsiang Yu & Wen-Ta Chiu & Carlos Lam, 2022. "Environmental Factors Associated with Severe Motorcycle Crash Injury in University Neighborhoods: A Multicenter Study in Taiwan," IJERPH, MDPI, vol. 19(16), pages 1-16, August.
    4. Khondoker Billah & Hatim O. Sharif & Samer Dessouky, 2021. "Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    5. Young, Jason & Park, Peter Y., 2014. "Hotzone identification with GIS-based post-network screening analysis," Journal of Transport Geography, Elsevier, vol. 34(C), pages 106-120.
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