Abstract
Vehicle emission analysis currently faces a trade-off between easy-to-use, low-accuracy macroscopic models, and computationally intensive, high-accuracy microscopic models. A surrogate model that leverages microscopic traffic and emission simulations to predict link-level emission rates was developed. The input variables were obtained by aggregating 1 Hz simulated vehicle trajectories into hourly traffic condition factors (e.g., link average/variation of speed, truck fleet percentage, road grade, etc.). The emission ground truth data were generated using the Motor Vehicle Emission Simulator opmode-based analysis module. Different parameter and machine learning model structures were examined to establish the statistical relationship of the input variables and the link-level emission rates. The ability of the model to accurately estimate vehicle-related emissions was demonstrated by using the Columbia, South Carolina road network as an example. This model served as a high-level planning tool to assess the impacts of emissions from transportation projects.
| Original language | English |
|---|---|
| Pages (from-to) | 778-789 |
| Number of pages | 12 |
| Journal | Journal of the Air and Waste Management Association |
| Volume | 71 |
| Issue number | 6 |
| DOIs | |
| State | Published - 2021 |
| Externally published | Yes |
Funding
This study is based upon work supported by the USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier 1 University Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDOT Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof. This study is based upon work supported by the USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier 1 University Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDOT Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof. This study is based upon work supported by the USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier 1 University Transportation Center) headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDOT Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof.