| 摘要 |
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.
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