Journal article 32 views
Unveiling built environment impacts on traffic CO2 emissions using Geo-CNN weighted regression
,
Salvatore Antonio Biancardo,
Xiaolei Ma
Transportation Research Part D: Transport and Environment, Volume: 132, Start page: 104266
Swansea University Author:
Yue Hou
Full text not available from this repository: check for access using links below.
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...
| Published in: | Transportation Research Part D: Transport and Environment |
|---|---|
| ISSN: | 1361-9209 |
| Published: |
Elsevier BV
2024
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa67683 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 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. |
|---|---|
| College: |
Faculty of Science and Engineering |
| Funders: |
The study is supported by National Key R&D Program of China (2023YFB2604600) and Beijing Nova Program (No. 20230484432). |
| Start Page: |
104266 |