Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting

Traffic forecasting is of great significance and has many applications in Intelligent Traffic System (ITS). In spite of many thoughtful attempts in the past decades, this task still remains far from being solved, due to the diversity, complexity and nonlinearity of traffic situations. Technically, it can be cast on the framework of regressions with spatial-template data. Typically, one may consider to employ the Convolutional Neural Network (CNN) to achieve this goal. Unfortunately, the traditional CNN is developed for grid data. By contrast, here we are facing with non-grid traffic data points that are observed spatially at locations of interest. To this end, this paper proposes a novel Kernel-Weighted Graph Convolutional Network (KW-GCN) for traffic forecasting, which learns simultaneously a group of convolutional kernels and their linear combination weights for each of the nodes in the graph. This yields a mechanism that is able to learn the features locally and exploit the structure information of traffic road-network globally. By introducing additional parameters, our KW-GCN can relax the restriction of weight sharing in classical CNN to better handle the traffic data of non-stationarity. Furthermore, it has been illustrated that the proposed linear weighting of kernels can be viewed as the low-rank decomposition of the well-known locally-connected networks, and thus it avoids over-fitting to some degree. We apply our approach to the real-world GPS data set of about 30,000 taxis in seven months in Beijing. Experiments on both taxi-flow forecasting and road-speed forecasting demonstrate that our method significantly outperforms the state-of-the-art ones.