利用建筑垃圾运输车辆GPS数据对土方工程相关地点进行分类：成都案例研究
Using construction waste hauling trucks' GPS data to classify earthwork-related locations: A Chengdu case study
AI总结：
本文提出利用成都1.6万余辆建筑垃圾运输车GPS轨迹及58项城市特征，通过机器学习识别分类土方工程相关地点，并在Alpha MAPS中落地辅助城市粉尘治理。
摘要：
土方工程相关地点（ERLs），如建筑工地、弃土场和混凝土搅拌站，是城市粉尘污染（颗粒物）的主要来源。对ERLs进行有效管理至关重要，需要在全市范围内及时、高效地追踪这些地点。本研究旨在利用超过16000辆建筑垃圾运输车辆（CWHTs）的GPS轨迹数据，以及涵盖地理、土地覆盖、POI和交通维度的58项城市特征，识别并分类城市ERLs。我们在中国成都的真实世界数据上比较了多种机器学习模型，并考察了各类时空特征对分类性能的影响。结果表明，仅使用数量有限的特征即可达到77.8%的分类准确率。该分类框架已在成都的Alpha MAPS系统中实施，并于2023年12月在全市成功识别出724处建筑工地/弃土场、48座混凝土搅拌站和80处卡车停放地点，使当地主管部门能够以较低的人力成本有效管理城市粉尘污染。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
