Video based Traffic Congestion Prediction on an Embedded System

In recent years, computer vision methods have been exploited in traffic surveillance systems to perform video image analysis, e.g. extracting statistical traffic information and detecting events. However, not much work was dedicated to the prediction of events, in particular of traffic congestions. This paper has two contributions: First, it presents an embedded computer vision system which collects traffic data, and secondly, it reports an innovative method for predicting traffic congestions. For the latter purpose, three traffic parameters are measured and analysed: average speed, vehicle density and the amount of lane changes. The novelty of the current work resides in the use of lane changes in order to predict a traffic congestion. It is shown how the amount of lane changes can be used for improving the prediction of a traffic congestion event some minutes before the traffic congestion starts. The validity of the proposed method is tested using data from a real scenario, which have been collected by the embedded computer vision system also presented in this work. The obtained results are discussed, along with possible future improvements and new research directions.