Title

Exploring The Safety Application Of Automatic Vehicle Identification Data

Abstract

Most common application of Automatic Vehicle Identification (AVI) systems is electronic toll collection and travel time estimation. There is a promising traffic safety application in the context of Active Traffic Management systems. The operation-based management of expressways can benefit from the collected data not only to enhance operation but also to provide warnings of increased risk situations. This study examines the usefulness of traffic data collected from the AVI system on I-70 in Colorado in real-time crash prediction. Ten-minute average speed at the crash segment during 5-15 minutes prior to the time of the crash as well as the average visibility during one hour prior the crash time are found to be the most significant factors affecting the crash likelihood on I-70. The risk for a crash increases 6.5 percent for each unit decrease in the ten minute average speed, and it increases 37% for each unit decrease in the average visibility measured over one hour before the crash time. The developed statistical framework has the capability of monitoring the traffic and weather conditions and detects the turbulence in the traffic and any reduction in the visibility that may lead to a crash in real-time. The achieved classification accuracy from the model using AVI data is comparable to previous models used inductive loop detectors data. The findings of this study illustrate a promising real-time safety application for one of the most widely used and already present ITS systems, with many possible advances in the context of Advanced Traffic Management.

Publication Date

12-1-2012

Publication Title

Proceedings, Annual Conference - Canadian Society for Civil Engineering

Volume

2

Number of Pages

1702-1711

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84875507894 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84875507894

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