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Detecting urban traffic congestion with single vehicle

Traffic congestion in urban areas is a severe problem in many cities around the world. Conventional infrastructure-based solutions to detect traffic congestion, such as surveillance cameras and road surface inductive loops, have the limitations of high deployment costs and limited coverage. In recent years, due to the popularity of mobile devices, solutions that do not require pre-deployed infrastructure start to emerge; in these solutions, sensor data is collected by mobile devices onboard the vehicles, sent to a central server via vehicle-to-infrastructure (V2I) or cellular communications, and used collectively to determine the traffic states of the roads. However, existing solutions require data from a considerably large number of vehicles on the same road to accurately detect traffic congestion of a particular road. In this paper, we propose a novel approach to detect the traffic states of the roads with only the data from a single vehicle. The biggest advantage of such an approach is that, unlike previously proposed solutions, the system can function properly even if there is only a smaller number of vehicles equipped with the system, which is usually the case at the early stage of the deployment of a vehicle-to-vehicle (V2V) network or a large-scale intelligent transportation system. In our solution, machine learning mechanisms are utilized to classify the traffic state by extracting the movement behaviors of a vehicle. Our model development and performance evaluation utilize highly accurate vehicle traces collected at several real-world intersections with lidar. In addition, to properly label the obtained data traces to either congested or free-flow and accurately reflect the reality, a previously proposed theoretical method is used in combination with human labeling. Evaluation shows that our approach can achieve a detection accuracy of 88.94%.