This report describes the development of an algorithm to detect anomalies in the time series from inductance loop sensors. The algorithm uses a statistic produced with inductance loop data to make an optimal prediction of the volume and occupancy values that will occur at the next time step. Anomaly detection is accomplished by applying thresholds to the difference between the predictions and the observed values. The report demonstrates the use of the anomaly detection algorithm with inductance loop data gathered on Interstate 5 in Seattle, Washington. The report also discusses the scaling and values of threshold necessary for anomaly detection.
October 17, 2007
Daniel J. Dailey.
Washington State Transportation Center (TRAC)
- # of Pages: 28 p., 389 KB (PDF)
- Subject: Algorithms, Fault location, Highway traffic control, Traffic flow, Travel time, Kalman filtering, Loop detectors.
- Keywords: Induction loops, volume occupancy, Kalman filter, anomaly detection, traffic, I-90, Seattle (Wash.)
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This abstract was last modified April 29, 2008