Timely Prediction of Road Traffic Congestion Using Ontology

In developing countries, traffic in a road network is a major issue. In this paper we investigate the tradeoff between speed versus accuracy of predicting the severity of road traffic congestion. The timely prediction of traffic congestion using semantic web technologies that will be helpful in various applications like better road guidance, vehicle navigation system. In the proposed work, ontology is created based on sensor and video data. By using rule inference of ontology on parallel processing of sensor and video data, our system gives the timely prediction of traffic congestion.