Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait

Understanding commuter traffic in transportation networks is crucial for sustainable urban planning with commuting generation forecasts operating as a pivotal stage in commuter traffic modeling. Overcoming challenges posed by the intricacy of commuting networks and the uncertainty of commuter behaviors, we propose MetroGCN, a metropolis-informed graph convolutional network designed for commuting forecasts in metropolitan areas. MetroGCN introduces dimensions of metropolitan indicators to comprehensively construct commuting networks with diverse socioeconomic features. This model also innovatively embeds topological commuter portraits in spatial interaction through a multi-graph representation approach capturing the semantic spatial correlations based on individual characteristics. By incorporating graph convolution and temporal convolution with a spatial–temporal attention module, MetroGCN adeptly handles high-dimensional dependencies in large commuting networks. Quantitative experiments on the Shenzhen metropolitan area datasets validate the superior performance of MetroGCN compared to state-of-the-art methods. Notably, the results highlight the significance of commuter age and income in forecasting commuting generations. Statistical significance analysis further underscores the importance of anthropic indicators for commuting production forecasts and environmental indicators for commuting attraction forecasts. This research contributes to technical advancement and valuable insights into the critical factors influencing commuting generation forecasts.