Freeway Crash Predictions Based on Real-Time Pattern Changes in Traffic FlowCharacteristics

In recent years, attempts were made to develop a crash prediction model based on real-time detector data. Since studies in this field are primarily theoretical, improvements can be made in various aspects. It is expected that the final product of this study will be a program that integrates with the Advance Traffic Management System so that operators of Smart Travel Centers can take action to prevent or at least reduce the chances of crash occurrence. At this first stage, efforts were made to identify the crash leading patterns and the factors describing the patterns. Crashes that occurred on interstate highway basic segments between July 1, 2003 and June 30, 2004 from Northern Virginia were obtained from police crash reports. The associated traffic conditions as well as the normal non-crash conditions defined by the traffic parameters were collected from Smart Travel Lab. By applying three different pattern recognition techniques - the K-means clustering method; Naive-Bayes method; and Discriminant Analysis - it was found that the overall classification error rate remained at about 50% and was unable to identify the crash leading patterns.