Factors affecting injury severity : Different weather conditions analysis by multinomial logit model

Abstract: This paper focuses on the factors affecting motorcycle crash in weather conditions with their injury severity level using multinomial logit model. The data used for this paper are 5,791 pedestrian crash severities from police-reported crashes collected from Chicago in 2011 and 2012. The available crash data contained the common types of information on traffic crashes such as the time, location, and severity of crashes, as well as data on roadway and environmental conditions, driver characteristics, and crash attributes (such as major cause of crash). Multinomial logit model was used identify the factors leading to different injury severity level for motorcycle crash at different weather conditions i.e. clear, cloudy and rainy. For this, stacking the data was the starting process. Then assuming some variables that can influence the injury levels were added one by one to get the results. The results indicated that there are 17 significant variables in total with more than 90% confidence interval. For, none injury severity level, the chance of getting none injury tend to decrease if the driver wearing helmet. In possible injury severity level, if the driver is wearing helmet and the vehicle is moving straight, the chances of possible injury are more likely to increase. For the fatal injury, with increase in vehicle’s speed and if the impact area is front, the chance in get into fatal injury are more likely to increase while the straight road without grade is seem to decrease the chance of fatal injury severity level.