Metro passenger flow prediction based on the adaptive diffusion graph convolution attention network
Accurate metro passenger flow prediction is an important strategic requirement for intelligent transportation systems to address traffic challenges,coordinate operational scheduling,and plan future developments.However,previous research that integrated graph convolutional networks with deep learning models such as recurrent neural networks,long short-term memory networks,and gated recurrent neural networks,could only extract temporal spatial correlations based on the road network map structure,while ignoring the hidden spatial correlations between metro stations and the dynamic temporal correlations over time.To mine the complex spatial and temporal correlations in the transportation data to achieve accurate metro passenger flow prediction,a method based on Adaptive Diffusion Graph Convolution Attention(ADGCA)network was proposed.The innovations of this method mainly include two aspects:first,by constructing multiple graphs and adaptive matrices combined with multi-head attention mechanisms,it is able to mine the hidden spatial correlations between metro stations.This approach optimized the inadequacy of existing methods in extracting the spatial information features of metro systems,which made the ADGCA model able to extract the spatial information features in the metro system.Second,a deep learning model component combining causal convolution,adaptive diffusion map convolution and multi-head attention mechanisms was constructed.The component can capture the dynamic spatio-temporal correlations in metro passenger flow data at both local and global levels,and is more effective in extracting complex metro passenger flow data features than previous methods.The effectiveness of the model was evaluated on two real datasets constructed from passenger swipe records of the automatic metro ticketing systems in Shanghai and Hangzhou.The research results indicate that the ADGCA model can extract more realistic dynamic spatio-temporal correlations compared to existing baseline models,thereby effectively reducing prediction error.The prediction accuracy indices of the ADGCA model are better than the baseline model in all prediction time steps.The research findings provide more precise data support for further optimizing urban metro operation plans and ensuring the safe and efficient operation of metros.
万方数据
维普
