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

Abstract

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 of the model is improved; A graph convolutional network is adopted to model a road network topological structure, and graph embedding is adopted to model road network semanticassociation, so that the road network topological structure and the semantic association can be processed by a deep neural network.

Description

technical field [0001] The invention relates to machine learning technology, in particular to a congestion index prediction method. Background technique [0002] The intelligent transportation system can collect the average vehicle speed of the road through coils, microwaves, cameras and other equipment, and then calculate the road congestion index, and the congestion index prediction refers to predicting its future congestion index based on the historical congestion index of the road. Congestion index prediction is of great significance to travel planning and traffic control. [0003] Congestion index prediction methods mainly include knowledge-driven methods and data-driven methods. The knowledge-driven method is a more traditional method, which mainly realizes prediction by simulating the operation of vehicles. A data-driven approach is a method to achieve predictions based on advanced machine learning techniques. Since the congestion index is a kind of time series dat...

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