Importance

Loading Importance Indicators...

Abstract

A traffic incident detection system (10) includes both the collection and analysis of traffic data and employs a time-indexed traffic anomaly detection algorithm which partitions time into categories of 'type of day,' and 'time of day'. Using this partition, a fuzzy neuromorphic, unsupervised learning algorithm calibrates fuzzy sets as 'normal' and 'abnormal' for a plurality of traffic descriptors. Fuzzy composition techniques are used, on a per traffic lane basis, to combine multiple traffic descriptors in order to determine membership in a 'normal' or 'abnormal' lane status. Each lane status is then combined to determine the overall status of a road segment. Initial training of the algorithm occurs during the first few weeks after a sensor (12) is installed. On-line background training continues thereafter to continually tune and track seasonal changes affecting system performance.

Loading the Abstract Image...

First Claim