A congestion index prediction method combining a road network topological structure and semantic association

The invention discloses a congestion index prediction method combining a road network topological structure and semantic association. The method comprises the following steps: (1) establishing an undirected graph based on a space topological structure of a road network; (2) firstly calculating the similarity between the historical congestion index data of the road, then establishing a weighted undirected graph based on the similarity, and finally embedding the weighted undirected graph to obtain a semantic vector for representing the road; And (3) extracting short-term congestion index changecharacteristics on the basis of the graph convolutional network, extracting long-term congestion index change characteristics on the basis of the recurrent neural network, and fusing road semantic vectors on the basis to establish a prediction model. According to the method, spatial topology association and historical semantic association of the road network are considered at the same time, and the prediction capability o