A congestion prediction method based on trajectory mining algorithm

Abstract Nowadays, number of private cars is increasing rapidly. Traffic congestion becomes a serious problem in urban region. If traffic congestion can be predicted before it happens, it will be helpful for improving traffic condition. So many traffic congestion prediction methods have been proposed. Almost all these methods are based on traffic flow prediction algorithm. In these methods, historical traffic flow data is used while performing prediction. Obviously, information of sudden accidents like traffic accidents, road damage and bad weather that happened recently may be not contained in historical traffic flow data. But performance of traffic flow prediction algorithms will be affected by these factors. In this situation, performance of traffic congestion prediction method based on traffic flow prediction result will be affected as well. To solve the problem, a new traffic congestion prediction method based on trajectory mining algorithm is proposed in this paper. In this method, traffic controllers can set a threshold for each road according to the current situation of the road. The threshold represents the vehicle number that can be carried by the corresponding road in a short period. Besides, for each road, the proposed method tries to count the number of vehicles that will pass through the specific road at next time step by predicting next location for all the running vehicles based on their trajectories. If the vehicle number of a road surpasses the threshold of this road, it will be predicted as congested road. Otherwise, it will be predicted as non-congested road.