Traffic Incident Detection Based on Rough Sets Approach

This paper presents an approach to detect traffic incident which uses the rules generated by rough sets theory to classify traffic patterns for incident detection. Performance metrics such as detection rate, false alarm rate, mean time to detection and classification rate are computed. By way of illustration, a simulated traffic data set which is balanced and the real 1-880 freeway traffic data collected in California which is imbalanced are used to assess the detection performance of this approach. Rough sets method is sensitive to attributes discretization proven by the experimental results, so cross validation was used to conduct the discrete operation in order to improve the classification accuracy. Further tests also indicate that rules filter can enhance the performance of classification. Our experiments illustrate the incident detection models based on rough sets theory have favorable performance compared with those based on support vector machine. At last, a brief conclusion as well as future research needed is also discussed.