Estimating link level traffic emissions: enhancing MOVES with open-source data
Department of Civil and Environmental Engineering（土木与环境工程系）；Northeastern University（东北大学）；Massachusetts Institute of Technology（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）
Lijiao Wang, Muhammad Usama, Haris N. Koutsopoulos, Zhengbing He
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.
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach
University of Central Florida（中佛罗里达大学）；Amadeus（Amadeus（阿莫迪乌斯，全球旅游科技公司））
IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing
Arizona State University（亚利桑那州立大学）；School for Engineering of Matter, Transport and Energy（物质、运输与能源工程学院）；School of Computing and Augmented Intelligence（计算与增强智能学院）
