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Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track
One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Give...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742016/ https://www.ncbi.nlm.nih.gov/pubmed/36532156 http://dx.doi.org/10.1186/s40068-022-00276-2 |
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author | Biramo, Zelalem Birhanu Mekonnen, Anteneh Afework |
author_facet | Biramo, Zelalem Birhanu Mekonnen, Anteneh Afework |
author_sort | Biramo, Zelalem Birhanu |
collection | PubMed |
description | One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO(2)) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PM(x)) emissions by 28.12%, nitrogen oxides (NO(x)) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level. |
format | Online Article Text |
id | pubmed-9742016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-97420162022-12-12 Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track Biramo, Zelalem Birhanu Mekonnen, Anteneh Afework Environ Syst Res (Heidelb) Research One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO(2)) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PM(x)) emissions by 28.12%, nitrogen oxides (NO(x)) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level. Springer Berlin Heidelberg 2022-12-12 2022 /pmc/articles/PMC9742016/ /pubmed/36532156 http://dx.doi.org/10.1186/s40068-022-00276-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Biramo, Zelalem Birhanu Mekonnen, Anteneh Afework Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title | Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title_full | Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title_fullStr | Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title_full_unstemmed | Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title_short | Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
title_sort | modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9742016/ https://www.ncbi.nlm.nih.gov/pubmed/36532156 http://dx.doi.org/10.1186/s40068-022-00276-2 |
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