Urban short-term traffic speed prediction with complicated information fusion on accidents

Abstract

Optimizing the traffic flow prediction system is crucial in developing intelligent transportation since it increases the road network's capacity. The system's overall prediction accuracy will be increased by taking into account the relationship between the temporal and spatial properties of the road network and different external elements affecting the traffic situation. The traffic state, which is still a largely unexplored area, is impacted by the complicated interaction between accident information and the spatiotemporal properties of the route. This paper proposes an Accident Information Graph Fusion Attention Convolutional Network(AI-GFACN). Firstly, a highly correlated global road network is created using a global spatial feature point-edge swapping method, a D–D algorithm fusing Dijkstra, and Depth-First Search, which resolves the issue where the spatial features of accident sections are challenging to capture the diffusion effects caused by spatial features of nearby and further sections. Following the data's incorporation, it is suggested to combine the Spatio-temporal features of accident information and embed them in the road network. In addition, an attention mechanism is introduced, effectively addressing the difficulty in capturing the Spatio-temporal features of accident information within the road network. By integrating and categorizing the regionally distributed and temporally sustained congestion effects of various categories of accidents concerning previous research on accident information, this paper enhances the semantic expressiveness of accident information within the road network. Ablation experiments confirm the effectiveness and robustness of the proposed method, and it is applied to the dataset of Hangzhou West Lake District (including accident information), which increases short-term traffic speed prediction accuracy by 0.2% overall.