Bivariate-Logit-Based Severity Analysis for Motorcycle Crashes in Texas, 2017–2021
نویسندگان
چکیده
Due to the number of severe traffic collisions involving motorcycles, a comprehensive investigation is required determine their causes. This study analyzed Texas crash data from 2017 2021 who was at fault and how various factors affect frequency severity motorcycle collisions. Moreover, tried identify high-risk sites for crashes. Utilizing bivariate analysis logistic regression models, investigated individual combined effects several variables. Heat maps hotspot analyses were used locations with high incidence both minor The survey showed that dangerous speed, inattention, lane departure, failing surrender right-of-way stop sign or during left turn leading causes When motorcyclist fault, likelihood much higher. revealed numerous elements as strong predictors catastrophic crashes, including higher speed limits, poor illumination, darkness weekend, dividers designated lanes principal road control, an increased age primary victim, lack helmet. concentration found be relatively in city cores, whereas clusters detected on segments beyond limits. recommends implementing reduced limits segments, mandating helmet use, prioritizing resource allocation locations, launching educational campaigns promote safer driving practices use protective gear, inspecting existing conditions well geometry reduce
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ژورنال
عنوان ژورنال: Sustainability
سال: 2023
ISSN: ['2071-1050']
DOI: https://doi.org/10.3390/su151310377