Urban form, traffic volume, and air quality

A spatiotemporal stratified approach

Bibliographic Data

ID21248130
AuthorsYe Tian (0000-0003-0545-942X, University of Georgia, corresponding author), Xiaobai A Yao (0000-0003-2719-2017, University of Georgia), Xiaobai Yao (University of Georgia)
Year2022
Volume49
Issue1
Pages92-113
Publication date2022-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/2399808321995822
OpenAlexW3134395332
LanguageEN
Citations received3
References cited37

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

Air quality index · Civil engineering · Data mining · Decision tree · Geography · Meteorology · Random forest · Traffic volume · Transport engineering · Urban planning · Air Quality and Health Impacts · Computer Science · Engineering · Environmental Science · Urban Transport and Accessibility · Vehicle emissions and performance · Artificial Intelligence

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