Likelihood Parameterization of Bicycle Crash Injury Severities

Abstract

This paper evaluates different factors and parameters contributing to likelihood of bicycle crash injury severity levels. Multinomial Logit (MNL) model was used to analyze impact of different roadway features, traffic characteristics and environmental conditions associated with bicycle crash injury severities. The multinomial model was used due to its flexibility in quantifying the effect of the independent variables for each injury severity categories. Model results showed that, severity of bicycle crashes increases with increase in vehicles per lane, number of lanes, bicyclist alcohol or drug use, routes with 35-45 mph posted speed limits, riding along curved or sloped road sections, when bicyclists approach or cross a signalized intersection, and at driveways. In addition, routes with a high percentage of trucks, roadway sections with curb and gutter, cloudy or foggy weather and obstructed vision were found to have high probability of severe injury. Segments with wider lanes, wide median and wide shoulders were found to have low likelihood of severe bicycle injury severities. Limited lighting locations was found to be associated with incapacitating injury and fatal crashes, indicating that insufficient visibility can potentially lead to severe crashes. Other findings are also presented in the paper.