Improving Safety and Security by Developing a Traffic Accident Prevention System

Abstract

This research aims at developing real-time accident prediction models to be incorporated in Advanced Traffic Management Systems (ATMS). By reducing incident related congestion on freeways, response and evacuation times would also be reduced in emergency situations. The data from a 13.25 mile segment of Interstate in Central Florida equipped with loop detectors have been used. Preliminary analysis of detailed real-time speed data showed changes in speed upstream of accidents. Substantial variation in speed before the accident (both space and time) are found to be significant when compared to cases that experienced no accidents. Logistic regression has been adopted and showed that the 5-minute average occupancy observed at the upstream station during 5-10 minute prior to the accident along with the 5-minute coefficient of variation in speed at the downstream5 station during the same time have been found to affect the accident occurrence most significantly. This paper proves that real-time freeway loop detector data could be used in predicting accident likelihood 5-10 minutes before they occur. Therefore, Advanced Traffic Management Centers could attempt to prevent accidents by disseminating warning messages or adopting Variable Speed Limit techniques.