Using Construction Waste Hauling Trucks' GPS Data to Classify Earthwork-Related Locations: A Chengdu Case Study

Impact Factor:5.7

DOI number:10.1109/TBDATA.2025.3575940

Journal:IEEE Transactions on Big Data

Abstract: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.

Indexed by:Journal articles

Volume:11

Issue:6

Page Number:3130-3141

Translation or Not:no

Date of Publication:2025-06-06

Included Journals:SCI

Links to published journals:https://ieeexplore.ieee.org/document/11027757