用开源数据提升交通排放估算精度，比传统方法误差降超50%
Estimating link level traffic emissions: enhancing MOVES with open-source data
- 融合GPS轨迹、道路网络与卫星数据，训练神经网络预测车辆运行状态
- 在波士顿45个市镇验证，对CO、NOx等主要污染物排放估算误差降超50%
- 全开源方案低成本可复现，适合城市环境政策制定者使用
开源数据为城市区域车辆活动与排放估算提供了可扩展且透明的基础。本研究提出一种数据驱动框架，整合MOVES模型与开源GPS轨迹数据、OpenStreetMap（OSM）道路网络、区域交通数据集及卫星影像衍生特征向量，以估算路段级车辆运行模式分布与交通排放。通过仅使用易获取的数据特征，训练神经网络模型预测MOVES定义的运行模式分布。该方法在波士顿都会区45个市政区域的应用中，以OSM开源GPS轨迹作为“真实”运行模式基准。相比MOVES基线，新模型在区域尺度上对CO、NOx、CO2和PM2.5等关键污染物的排放估算，均实现超过50%的均方根误差（RMSE）降低。研究表明，利用完全开源数据源进行低成本、可复现、数据驱动的排放估算具有可行性。
原文摘要 · Abstract (English)
Open-source data offers a scalable and transparent foundation for estimating vehicle activity and emissions in urban regions. In this study, we propose a data-driven framework that integrates MOVES and open-source GPS trajectory data, OpenStreetMap (OSM) road networks, regional traffic datasets and satellite imagery-derived feature vectors to estimate the link level operating mode distribution and traffic emissions. A neural network model is trained to predict the distribution of MOVES-defined operating modes using only features derived from readily available data. The proposed methodology was applied using open-source data related to 45 municipalities in the Boston Metropolitan area. The "ground truth" operating mode distribution was established using OSM open-source GPS trajectories. Compared to the MOVES baseline, the proposed model reduces RMSE by over 50% for regional scale traffic emissions of key pollutants including CO, NOx, CO2, and PM2.5. This study demonstrates the feasibility of low-cost, replicable, and data-driven emissions estimation using fully open data sources.
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