A special event-based K-nearest neighbor model for short-term traffic state prediction

Abstract

Recently, short-term traffic state prediction for urban transportation networks has become a popular topic. However, due to the uncontrollable and unpredictable elements of special events, it is difficult to get abundant data and desired predictions under such condition. As k-nearest neighbor (KNN) has a competitive advantage over other approaches, it could predict traffic state based on a small correlative part of data. Thus, a special event-based KNN (SEKNN) model is proposed for the short-term traffic state prediction with three key points presented in this paper. First, the evolution of the traffic states is redefined as a multipart object, state unit, which includes the benchmark state and the trend vector. Second, to select the nearest neighbors, the state distances of the state units are designed to be compatible with the benchmark states and the trend vectors by fusing the Euclidean distance and the cosine distance. Finally, the prediction results are forced to adjust the benchmark states based on the prediction function using the Gaussian weighted method. The proposed SEKNN is implemented in the district of the Beijing Workers' Stadium (257 links), where special events occur frequently. The results show that the proposed model performs significantly better under special events than the other traditional machine-learning approaches and state-of-the-art deep-learning approaches.