Urban traffic flow prediction: a spatio-temporal variable selection-based approach

Short-term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio-temporal-based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high-density road networks. Therefore, we propose a spatio-temporal variable selection-based support vector regression (VS-SVR) model fed with the high-dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two-stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high-dimensional spatio-temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real-world traffic volume collected from a sub-area of Shanghai, China, demonstrate that the proposed spatio-temporal VS-SVR model outperforms the state-of-the-art. Copyright (c) 2015 John Wiley & Sons, Ltd.