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Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression

Bing Liu, Feng Li, Yue Hou Orcid Logo, Salvatore Antonio Biancardo, Xiaolei Ma

Transportation Research Part D: Transport and Environment, Volume: 132, Start page: 104266

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1016/j.trd.2024.104266

Abstract

Understanding the associations between the built environment and road traffic CO2 emissions is crucial for developing strategies to mitigate carbon emissions. However, previous research struggled to capture complex spatial relationships accurately due to classical geospatial models’ limitations and...

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Published in: Transportation Research Part D: Transport and Environment
ISSN: 1361-9209
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67683
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first_indexed 2024-09-13T10:39:04Z
last_indexed 2024-09-13T10:39:04Z
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spelling v2 67683 2024-09-13 Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2024-09-13 ACEM Understanding the associations between the built environment and road traffic CO2 emissions is crucial for developing strategies to mitigate carbon emissions. However, previous research struggled to capture complex spatial relationships accurately due to classical geospatial models’ limitations and the challenges of estimating CO2 emissions from operational vehicle data or limited sample sizes. Therefore, we introduce a novel model that leverages extensive vehicle trajectory data for estimating road traffic CO2 emissions. Furthermore, we develop a geographically convolutional neural network weighted regression (GCNNWR) model to analyze the correlation between the built environment and these emissions. This model employs convolutional neural networks to effectively capture non-linear spatial relationships. An empirical analysis was conducted in Beijing, China, demonstrating the superiority of the GCNNWR model in accommodating spatial heterogeneity compared to conventional geospatial models. Our findings provide critical insights into optimizing the built environment to minimize CO2 emissions. Journal Article Transportation Research Part D: Transport and Environment 132 104266 Elsevier BV 1361-9209 Road traffic CO2 emissions; Spatial–temporal characteristics; Built environment; Geographically weighted regression model; Convolutional neural network 1 7 2024 2024-07-01 10.1016/j.trd.2024.104266 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University The study is supported by National Key R&D Program of China (2023YFB2604600) and Beijing Nova Program (No. 20230484432). 2024-10-24T14:11:17.0894464 2024-09-13T11:37:14.7244429 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Bing Liu 1 Feng Li 2 Yue Hou 0000-0002-4334-2620 3 Salvatore Antonio Biancardo 4 Xiaolei Ma 5
title Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
spellingShingle Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
Yue Hou
title_short Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
title_full Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
title_fullStr Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
title_full_unstemmed Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
title_sort Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Bing Liu
Feng Li
Yue Hou
Salvatore Antonio Biancardo
Xiaolei Ma
format Journal article
container_title Transportation Research Part D: Transport and Environment
container_volume 132
container_start_page 104266
publishDate 2024
institution Swansea University
issn 1361-9209
doi_str_mv 10.1016/j.trd.2024.104266
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description Understanding the associations between the built environment and road traffic CO2 emissions is crucial for developing strategies to mitigate carbon emissions. However, previous research struggled to capture complex spatial relationships accurately due to classical geospatial models’ limitations and the challenges of estimating CO2 emissions from operational vehicle data or limited sample sizes. Therefore, we introduce a novel model that leverages extensive vehicle trajectory data for estimating road traffic CO2 emissions. Furthermore, we develop a geographically convolutional neural network weighted regression (GCNNWR) model to analyze the correlation between the built environment and these emissions. This model employs convolutional neural networks to effectively capture non-linear spatial relationships. An empirical analysis was conducted in Beijing, China, demonstrating the superiority of the GCNNWR model in accommodating spatial heterogeneity compared to conventional geospatial models. Our findings provide critical insights into optimizing the built environment to minimize CO2 emissions.
published_date 2024-07-01T14:11:15Z
_version_ 1813801048954896384
score 11.036706

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