Outlier mining based Automatic Incident Detection on urban arterial road

Nowadays, Floating Car Data (FCD), which is becoming an important way to acquire traffic information, has been widely taken to estimate speed or travel time on road. In this paper, we introduce the concept of outlier mining into Automatic Incident Detection (AID) based on FCD and propose a novel AID approach on urban arterial road. According to the characteristics of incident, feature vector is selected from both spatial analysis and temporal analysis. Then a multilevel detection method that consists of filtering, outlier detection, and delay monitoring is proposed. The evaluation on real incident data and FCD gives the result that DR = 81.5% while FAR = 1.83%, which proves that the approach can achieve considerable effectiveness.