Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

Abstract: Traffic prediction plays an important role in the realization of traffic control and scheduling tasks in intelligent transportation systems. With the diversification of data sources, re asonably using rich traffic data to model the complex spatial-temporal dependence and nonlinear characteristics in traffic flow are the key challenge for intelligent transportation system. In addition, clearly evaluating the importance of spatialtemporal features extracted from different data becomes a challenge. A Double Layer - Spatial Temporal Feature Extraction and Evaluation (DL-STFEE) model is proposed. The lower layer of DL-STFEE is spatialtemporal feature extraction layer. The spatial and temporal features in traffic data are extracted by multi-graph graph convolution and attention mechanism, and different combinations of spatial and temporal features are generated. The upper layer of DL-STFEE is the spatial-temporal feature evaluation layer. Through the attention score matrix generated by the high-dimensional self-attention mechanism, the spatial-temporal features combinations are fused and evaluated, so as to get the impact of different combinations on prediction effect. Three sets of experiments are performed on actual traffic datasets to show that DL-STFEE can effectively capture the spatial-temporal features and evaluate the importance of different spatial-temporal feature combinations. With the continuous acceleration of urbanization, the population and vehicle ownership are also increasing, resulting in traffic congestion and other problems. In order to improve the efficiency, sustainability and security of transportation network, intelligent transportation system (ITS) [1] is proposed and becomes an advancing research field. Traffic prediction is an important step in the development of intelligent transportation [2]. It 2 aims to predict future traffic conditions by integrating historical observation data and measurement information of road sensor networks.