Detecting abnormal vehicular dynamics at intersections based on an unsupervised learning approach and a stochastic model.

This investigation demonstrates an unsupervised approach for modeling traffic flow and detecting abnormal vehicle behaviors at intersections. In the first stage, the approach reveals and records the different states of the system. These states are the result of coding and grouping the historical motion of vehicles as long binary strings.