Using construction waste hauling trucks' GPS data to classify earthwork-related locations: A Chengdu case study
This work addresses urban dust pollution management for local authorities by enabling efficient tracking of ERLs, though it is incremental as it applies existing machine learning methods to a new dataset.
The study tackled the problem of identifying and classifying earthwork-related locations (ERLs) to manage urban dust pollution by using GPS data from over 16,000 construction waste hauling trucks and 58 urban features, achieving 77.8% classification accuracy and identifying 724 construction sites/earth dumping grounds, 48 concrete mixing stations, and 80 truck parking locations in Chengdu.
Earthwork-related locations (ERLs), such as construction sites, earth dumping ground, and concrete mixing stations, are major sources of urban dust pollution (particulate matters). The effective management of ERLs is crucial and requires timely and efficient tracking of these locations throughout the city. This work 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. This classification framework was implemented in the Alpha MAPS system in Chengdu, which has successfully identified 724 construction cites/earth dumping ground, 48 concrete mixing stations, and 80 truck parking locations in the city during December 2023, which has enabled local authority to effectively manage urban dust pollution at low personnel costs.