A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. (January 2016) - Cai, Pinlong; Wang, Yunpeng; Lu, Guangquan; Chen, Peng; Ding, Chuan; Sun, Jianping
Highlights: Improving the KNN model considering the relationship among road segments. Using equivalent distances to describe the contacts among road segments. Using spatiotemporal state matrices to describe the traffic states. The nearest neighbors are selected by Gaussian weighted Euclidean distance. The forecasting results are integrated by Gaussian weighted method. Abstract: The k -nearest neighbor (KNN) model is an effective statistical model applied in short-term traffic forecasting that can provide reliable data to guide travelers. This study proposes an improved KNN model to enhance forecasting accuracy based on spatiotemporal correlation and to achieve multistep forecasting. The physical distances among road segments are replaced with equivalent distances, which are defined by the static and dynamic data collected from real road networks. The traffic state of a road segment is described by a spatiotemporal state matrix instead of only a time series as in the original KNN model. The nearest neighbors are selected according to the Gaussian weighted Euclidean distance, which adjusts the influences of time and space factors on spatiotemporal state matrices. The forecasting accuracies of the improved KNN and of four other models are compared, and experimental results indicate that the improved KNN model is more appropriate for short-term traffic multistep forecasting than the other models are. This study also discusses the application of the improved KNN model in a time-varying traffic state.