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Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions
Heavy-duty trucks contribute a significant component of all transportation in cargo terminals, such as Shaanxi Province, China. The emissions from these vehicles are the primary source of carbon emissions during highway operations. While several studies have attempted to address emission issues by i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678448/ https://www.ncbi.nlm.nih.gov/pubmed/31337123 http://dx.doi.org/10.3390/ijerph16142514 |
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author | Zhang, Xiaodong Xu, Jinliang Li, Menghui Li, Qunshan Yang, Lan |
author_facet | Zhang, Xiaodong Xu, Jinliang Li, Menghui Li, Qunshan Yang, Lan |
author_sort | Zhang, Xiaodong |
collection | PubMed |
description | Heavy-duty trucks contribute a significant component of all transportation in cargo terminals, such as Shaanxi Province, China. The emissions from these vehicles are the primary source of carbon emissions during highway operations. While several studies have attempted to address emission issues by improving traffic operations, a few focused on the relationship between emissions and highway geometric design, especially for heavy-duty trucks. The primary goal of this research was to understand the impact of circular curve on carbon dioxide (CO(2)) emissions produced by heavy-duty diesel trucks. Firstly, appropriate parameters were specified in MOVES (motor vehicle emission simulator) model according to the geometrical characteristics. Fuel consumption, speed and location data were collected by hiring five skilled drivers on the automotive proving ground located at Chang’an University, Shaanxi Province. The associated carbon emission data were derived from fuel consumption data by applying the IPCC (Intergovernmental Panel on Climate Change) method. After this, the applicability of MOVES model was verified by the field experiment. Moreover, a multiple regression model for CO(2) emissions incorporated with roadway segment radius, circular curve length, and initial vehicle speed was established with data generated by the MOVES model. The proposed CO(2) emission model was also verified by field experiment with relative error of 6.17%. It was found that CO(2) emission had monotone decreasing property with radius increasing, and the minimum radius that influenced diesel CO(2) emission was 550 m. The proposed quantitative CO(2) emission model can provide a reference for low-carbon highway design, leading to environment-friendly transportation construction. |
format | Online Article Text |
id | pubmed-6678448 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66784482019-08-19 Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions Zhang, Xiaodong Xu, Jinliang Li, Menghui Li, Qunshan Yang, Lan Int J Environ Res Public Health Article Heavy-duty trucks contribute a significant component of all transportation in cargo terminals, such as Shaanxi Province, China. The emissions from these vehicles are the primary source of carbon emissions during highway operations. While several studies have attempted to address emission issues by improving traffic operations, a few focused on the relationship between emissions and highway geometric design, especially for heavy-duty trucks. The primary goal of this research was to understand the impact of circular curve on carbon dioxide (CO(2)) emissions produced by heavy-duty diesel trucks. Firstly, appropriate parameters were specified in MOVES (motor vehicle emission simulator) model according to the geometrical characteristics. Fuel consumption, speed and location data were collected by hiring five skilled drivers on the automotive proving ground located at Chang’an University, Shaanxi Province. The associated carbon emission data were derived from fuel consumption data by applying the IPCC (Intergovernmental Panel on Climate Change) method. After this, the applicability of MOVES model was verified by the field experiment. Moreover, a multiple regression model for CO(2) emissions incorporated with roadway segment radius, circular curve length, and initial vehicle speed was established with data generated by the MOVES model. The proposed CO(2) emission model was also verified by field experiment with relative error of 6.17%. It was found that CO(2) emission had monotone decreasing property with radius increasing, and the minimum radius that influenced diesel CO(2) emission was 550 m. The proposed quantitative CO(2) emission model can provide a reference for low-carbon highway design, leading to environment-friendly transportation construction. MDPI 2019-07-14 2019-07 /pmc/articles/PMC6678448/ /pubmed/31337123 http://dx.doi.org/10.3390/ijerph16142514 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Xiaodong Xu, Jinliang Li, Menghui Li, Qunshan Yang, Lan Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title | Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title_full | Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title_fullStr | Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title_full_unstemmed | Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title_short | Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO(2) Emissions |
title_sort | modeling impacts of highway circular curve elements on heavy-duty diesel trucks’ co(2) emissions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678448/ https://www.ncbi.nlm.nih.gov/pubmed/31337123 http://dx.doi.org/10.3390/ijerph16142514 |
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