Assessing the Effect of Weather States on Crash Severity and Type by Use of Full Bayesian Multivariate Safety Models

Abstract

Rather than investigate the isolated effects of individual weather elements on crash occurrence, this study investigated the aggregated effect of weather states, defined as a combination of various weather elements (e.g., temperature, snow, rain, and wind speed), on crash occurrence. The main argument was that a combination of weather elements might better represent a particular weather condition and subsequent safety outcome. Therefore, to explore the effect of various weather states on crash severity and type, this study defined 12 weather states, based on temperature, snow, rain, and wind speed, and developed multivariate safety models by using 11 years of daily weather and crash data for Edmonton, Alberta, Canada. The proposed models were estimated in a full Bayesian context via a Markov chain Monte Carlo simulation, while a posterior predictive approach assessed the models’ goodness of fit. Results suggested that property-damage-only (PDO) crashes increased by 4.5% to 45% because of adverse weather states and showed that PDO crashes were more affected by adverse weather states than were severe (injury and fatal) crashes. For crash type, adverse weather states were associated with an increase of 9% to 73.7% for all crash types, with the highest increase for run-off-the-road crashes. Duration of daylight was found to be significant and negatively related to all crash types and PDO crashes. Sudden weather changes of major snow or rain were statistically significant and positively related to all crash types. Days of the week and seasons of the year were used as dummy variables and were statistically significant in relation to crash occurrence.