Predicting the Severity of Median-Related Crashes in Pennsylvania by Using Logistic Regression

Abstract

Models of median-related crash severity were developed by using roadway inventory and crash records for Pennsylvania Interstate highways. Cross-median and median barrier crashes formed the sample of crash types considered. Data were collected to model crash severity, including cross-section, traffic volume, and environmental predictor variables. Logistic regression models were developed by using both an ordinal and a nominal response. The results indicate that modeling crash severity as an ordinal response provided appropriate results for cross-median crashes, whereas a nominal response was more appropriate for median barrier crashes. Explanatory variables such as pavement surface conditions, use of drugs or alcohol, presence of an interchange entrance ramp, horizontal alignment, crash type, and average daily traffic volumes affect crash severity. The analysis results may be used by practitioners to understand the trade-off between geometric design decisions and median-related crash severity. Approximately 0.7% median barrier crashes on the Interstate system resulted in a fatality, whereas 43% were property-damage-only crashes and about 56% were injury crashes. More than 17% of cross-median collisions were fatal, and 67% involved injury.