Estimating link level traffic emissions: enhancing MOVES with open-source data
This work addresses the need for scalable and transparent emissions estimation in urban planning and environmental monitoring, though it is incremental as it enhances an existing framework with new data sources.
The study tackled the problem of estimating vehicle emissions at the link level by integrating MOVES with open-source data like GPS trajectories and satellite imagery, resulting in a model that reduces RMSE by over 50% for pollutants such as CO, NOx, CO2, and PM2.5 compared to the MOVES baseline.
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.