A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting |
| |
Institution: | 1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China;2. Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, Guangdong, 515000, China;3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, 999077, China;4. College of Mathematics and Information, South China Agricultural University, Guangzhou, Guangdong, 510642, China;5. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, 999077, China |
| |
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. |
| |
Keywords: | Short-term traffic forecasting Spatiotemporal correlation Gaussian weighted Euclidean distance |
本文献已被 ScienceDirect 等数据库收录! |
|