DocumentCode :
1362635
Title :
The Viability of Using Automatic Vehicle Identification Data for Real-Time Crash Prediction
Author :
Ahmed, Mohamed M. ; Abdel-Aty, Mohamed A.
Author_Institution :
Dept. of Civil, Environ. & Constr. Eng., Univ. of Central Florida, Orlando, FL, USA
Volume :
13
Issue :
2
fYear :
2012
fDate :
6/1/2012 12:00:00 AM
Firstpage :
459
Lastpage :
468
Abstract :
Real-time crash prediction research attempted the use of data from inductive loop detectors; however, no safety analysis has been carried out using traffic data from one of the most growing nonintrusive surveillance systems, i.e., the tag readers on toll roads known as automatic vehicle identification (AVI) systems. In this paper, for the first time, the identification of freeway locations with high crash potential has been examined using real-time speed data collected from AVI. Travel time and space mean speed data collected by AVI systems and crash data of a total of 78 mi on the expressway network in Orlando in 2008 were collected. Utilizing a random forest technique for significant variable selection and stratified matched case-control to account for the confounding effects of location, time, and season, the log odds of crash occurrence were calculated. The length of the AVI segment was found to be a crucial factor that affects the usefulness of the AVI traffic data. While the results showed that the likelihood of a crash is statistically related to speed data obtained from AVI segments within an average length of 1.5 mi and crashes can be classified with about 70% accuracy, all speed parameters obtained from AVI systems spaced at 3 mi or more apart were found to be statistically insignificant to identify crash-prone conditions. The findings of this study illustrate a promising real-time safety application for one of the most widely used and already present intelligent transportation systems, with many possible advances in the context of advanced traffic management.
Keywords :
automated highways; road safety; surveillance; advanced traffic management; automatic vehicle identification data; automatic vehicle identification systems; expressway network; freeway locations; high crash potential; inductive loop detectors; intelligent transportation systems; nonintrusive surveillance systems; random forest technique; real-time crash prediction; real-time safety application; safety analysis; traffic data; Computer crashes; Detectors; Radio frequency; Real time systems; Traffic control; Vehicle crash testing; Vehicles; Automatic vehicle identification (AVI); freeway/expressway; intelligent transportation system (ITS); safety risk;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2011.2171052
Filename :
6061960
Link To Document :
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