{"ID":2860752,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03362","arxiv_id":"2510.03362","title":"Estimating link level traffic emissions: enhancing MOVES with open-source data","abstract":"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.","short_abstract":"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-de...","url_abs":"https://arxiv.org/abs/2510.03362","url_pdf":"https://arxiv.org/pdf/2510.03362v1","authors":"[\"Lijiao Wang\",\"Muhammad Usama\",\"Haris N. Koutsopoulos\",\"Zhengbing He\"]","published":"2025-10-03T02:22:56Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}