DOI码：10.1109/TBDATA.2025.3575940
发表刊物：IEEE Transactions on Big Data
摘要：Earthwork-related locations (ERLs), such as construction sites, earth dumping grounds, and concrete mixing stations, are major sources of urban dust pollution (particulate matter). The effective management of ERLs is crucial and requires timely and efficient tracking of these locations throughout the city. This study aims to identify and classify urban ERLs using GPS trajectory data of over 16,000 construction waste hauling trucks (CWHTs), as well as 58 urban features encompassing geographic, land cover, POI, and transport dimensions. We compare several machine learning models and examine the impact of various spatial-temporal features on classification performance using real-world data in Chengdu, China. The results demonstrate that 77.8% classification accuracy can be achieved with a limited number of features. Implemented in the Alpha MAPS system in Chengdu, this classification framework successfully identified 724 construction sites/earth dumping grounds, 48 concrete mixing stations, and 80 truck parking locations in the city in December 2023, enabling local authorities to manage urban dust pollution at low personnel costs effectively.
论文类型：期刊论文
卷号：11
期号：6
页面范围：3130-3141
是否译文：否
发表时间：2025-06-06
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/document/11027757