Road traffic anomaly detection method using non-isometric time/space division

A road traffic anomaly detection method using non-isometric time/space division, comprising: establishing time/space sub-segments; preprocessing past tracking data; preprocessing real-time tracking data; analyzing past tracking data and training an RNN model; analyzing real-time tracking data and extracting features; detecting anomalies; indicating the degree of severity of the anomalies; and evaluating system performance. The method utilizes road network density or peak hour traffic as the basis for the division of time/space ranges, and uses a large amount of past tracking data to train an RNN model. The difference between RNN model prediction values and actual values reflects differences in traffic states. The present method achieves real-time smart detection of road traffic anomalies and incidents on city roads, increases detection reliability, and is low in cost.