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Abstract

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.

Keywords

Urban form traffic volume air quality spatiotemporal stratified random forest

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Urban form,traffic volume,and air quality: A spatiotemporal stratified approach

Abstract

Keywords

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