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.

了解交通网络中的通勤交通对于可持续城市规划至关重要，而通勤发电预测是通勤交通建模的关键阶段。为了克服通勤网络的复杂性和通勤者行为的不确定性所带来的挑战，我们提出了 MetroGCN，一种大都市信息图卷积网络，专为大都市地区的通勤预测而设计。 MetroGCN引入都市指标维度，全面构建具有多样化社会经济特征的通勤网络。该模型还创新性地将拓扑通勤者肖像嵌入到空间交互中，通过多图表示方法捕获基于个体特征的语义空间相关性。通过将图卷积和时间卷积与时空注意力模块相结合，MetroGCN 能够熟练地处理大型通勤网络中的高维依赖性。对深圳都市区数据集的定量实验验证了 MetroGCN 与最先进方法相比的优越性能。值得注意的是，结果强调了通勤者年龄和收入在预测通勤一代中的重要性。统计显着性分析进一步强调了人为指标对于通勤生产预测和环境指标对于通勤吸引力预测的重要性。这项研究有助于技术进步，并为影响通勤发电预测的关键因素提供了宝贵的见解。