• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.7-15
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• 2010

Research of this paper focused on developing and demonstrating of algorithm with the figures of difference between historical traffic pattern data and real-time traffic data to decide on what the incident is. The aim of this dissertation is to develop incident detection algorithm which can be understood and modified easier to operate. To establish traffic pattern of this algorithm, weighted moving average method was applied. The basis of this method was traffic volume and speed of the same day and time at the same location based on 30-second raw data. The model was completed by a serious of steps of process-screening process of error data, decision of the traffic condition, comparison with pattern data, decision of incident circumstances, continuity test. A variety of parameter value was applied to select reasonable parameter. Results of application of the algorithm came out with figures of average detection rate 94.7 percent, 0.8 percent rate of misinformation and the average detection time 1.6 minutes. With these following results, the detection rate turned out to be superior compared with result of existing model. Applying the concept of traffic patterns was useful to gain excellent results of this study. Also, this study is significant in terms of making algorithm which theorized the decision process of actual operators.