RRCF: an abnormal pulse diagnosis factor for road abnormal hotspots detection

Road hotspots detection method is a key issue in the field of intelligent transportation research. Compared with normal hotspots caused by high traffic flow, abnormal hotspots, which are results of road accidents, perform an occurrence time random behavior and difficult to predict. Deducing from the pulse diagnosis method, in this paper, a region real-time congestion factor is constructed to realize road abnormal hotspots discovery. Taxi’s GPS data of Hangzhou City, China are employed to find abnormal pulse of road segment, while the relationship between proposed congestion factor and the real-time traffic data is discussed. Two accidental scenarios are built to verify the validity of the proposed method. The experiment results show that the proposed method performs well in real-time abnormal hotspot detection and analysis output could be useful in path planning and traffic management.