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Updated: Feb 23, 2026
07:30
A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
Published on: September 21, 2017
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Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency
Wen Cheng1, Gurdiljot Singh Gill1, Taha Sakrani1
1Department of Civil Engineering, California State Polytechnic University, Pomona, 3801 W. Temple Ave., Pomona, CA, 91768, United States.
Accident; Analysis and Prevention
|September 10, 2017
Summary
Motorcycle crash severity is influenced by weather. Higher temperatures decrease fatal crashes, while rain reduces all crash severities. Understanding these risks can improve road safety.
Area of Science:
- Traffic Safety
- Environmental Science
- Epidemiology
Background:
- Motorcycle crashes are a significant cause of motor vehicle fatalities.
- The impact of weather on motorcycle crash severity is under-researched.
Purpose of the Study:
- To investigate the influence of weather conditions on motorcycle crash severity.
- To analyze crash data from San Francisco across four severity levels.
Main Methods:
- Developed five Full Bayesian models to analyze crash data.
- Accounted for common correlations in crash data.
- Compared model fitness and predictive performance.
Main Results:
- Models with serial and severity parameter variations showed superior fit and predictive accuracy.
- Increased air temperature reduced fatal crash risk but increased other severities.
- Rainfall decreased the likelihood of crashes across all severity levels.
- Humidity showed no significant impact on crash severity.
Conclusions:
- Weather conditions, particularly temperature and rainfall, significantly impact motorcycle crash severity.
- Findings can inform transportation agencies to enhance road safety measures.
- Providing motorcyclists with weather-specific risk information can mitigate crash outcomes.
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