Real time crash risk prediction model on freeways under nasty weather conditions

The real-time traffic flow data, crash data, crash data and weather condition data were extracted and collected from a 23-km segment of freeway I-880 N in the state of California of the United States. A real-time crash risk prediction model on freeways was built based on the traffic flow data and weather data using the Logistic regression model. The data analysis results showed that the weather condition variables had significant impact on the likelihood of crash occurrence on freeways. The odds ratios for rainy and foggy days were 6.4 and 4.4 respectively, indicating that the crash risks for rainy and foggy days were 5.4 and 3.4 times respectively higher than that for clear days. A Logistic regression model was also built based on only real-time traffic flow data for the comparison purpose. The analysis results show that the prediction accuracy of the model with weather variables was 71.7%, while it was 66.5% without these variables. The weater condition variables significantly enhance the prediction accaracy of the real-time crash risk prediction model on freeway.