Unrecorded accidents detection on highways based on temporal data mining

Automatic traffic accident detection, especially not recorded by traffic police, is crucial to accident black spots identification and traffic safety. A new method of detecting traffic accidents is proposed based on temporal data mining, which can identify the unknown and unrecorded accidents by traffic police. Time series model was constructed using ternary numbers to reflect the state of traffic flow based on cell transmission model. In order to deal with the aftereffects of linear drift between time series and to reduce the computational cost, discrete Fourier transform was implemented to turn time series from time domain to frequency domain. The pattern of the time series when an accident happened could be recognized using the historical crash data. Then taking Euclidean distance as the similarity evaluation function, similarity data mining of the transformed time series was carried out. If the result was less than the given threshold, the two time series were similar and an accident happened probably. A numerical example was carried out and the results verified the effectiveness of the proposed method.