Understanding the interplay between urban form, traffic volume, and air quality is significant for urban planning and environmental sustainability. However, limited progress has been made in bringing effective urban planning strategies to help control traffic demand and resulting air pollutants. Therefore, this study aims to investigate the interrelation between urban form, traffic volume, and air quality with a spatiotemporal stratified method. The method extracts and preprocesses traffic volume data in spatial (polluted and unpolluted zones) and temporal (periods in holidays and workdays) dimensions. Three decision tree models (random forest, random tree, M5 model tree) and two comparison models (multiple linear regression, artificial neural network) are used to examine the relationships. The final results show that the spatiotemporal stratification approach effectively reveals the interrelations, and the random forest model outperforms the other models. Specifically, highly aggregated roads and industrial areas are more associated with traffic volume in polluted zones. The dominance of waterway and vegetation shows a strong association with traffic volume in unpolluted zones. The degree of association also varies significantly between workdays and holidays. Our spatiotemporal stratified approach reveals heterogeneous relationships between urban form, traffic volume, and air quality and provides insightful references on sustainable urban development.

了解城市形态，交通量和空气质量之间的相互作用对于城市规划和环境可持续性具有重要意义。但是，在提出有效的城市规划策略以帮助控制交通需求和由此产生的空气污染物方面，进展有限。因此，本研究旨在通过时空分层方法研究城市形态，交通量和空气质量之间的相互关系。该方法提取并预处理空间（污染和未污染区域）和时间（假日和工作日的时间段）维度的交通量数据。使用三个决策树模型（随机森林，随机树，M5模型树）和两个比较模型（多重线性回归，人工神经网络）来检查这些关系。最终结果表明，时空分层方法有效地揭示了相互关系，而随机森林模型的表现优于其他模型。具体而言，高度聚集的道路和工业区与污染区的交通量更相关。水道和植被的优势与无污染地区的交通量密切相关。关联程度在工作日和假日之间也有很大差异。我们的时空分层方法揭示了城市形态，交通量和空气质量之间的异质关系，并为可持续城市发展提供了有见地的参考。高度聚集的道路和工业区与污染区的交通量更多相关。水道和植被的优势与无污染地区的交通量密切相关。关联程度在工作日和假日之间也有很大差异。我们的时空分层方法揭示了城市形态，交通量和空气质量之间的异质关系，并为可持续城市发展提供了有见地的参考。高度聚集的道路和工业区与污染区的交通量更多相关。水道和植被的优势与无污染地区的交通量密切相关。关联程度在工作日和假日之间也有很大差异。我们的时空分层方法揭示了城市形态，交通量和空气质量之间的异质关系，并为可持续城市发展提供了有见地的参考。