Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification
As a first step towards decreasing greenhouse gas emissions originating from transportation, it is critical that we create efficient systems for monitoring individual travel patterns and the associated carbon footprints. To this end, this paper presents a CO<sub>2</sub>e emission estimat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10138385/ |
| _version_ | 1827930609457037312 |
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| author | Orla Brimacombe Luis C. Gonzalez Johan Wahlstrom |
| author_facet | Orla Brimacombe Luis C. Gonzalez Johan Wahlstrom |
| author_sort | Orla Brimacombe |
| collection | DOAJ |
| description | As a first step towards decreasing greenhouse gas emissions originating from transportation, it is critical that we create efficient systems for monitoring individual travel patterns and the associated carbon footprints. To this end, this paper presents a CO<sub>2</sub>e emission estimator that combines transportation mode classification with mode-specific emissions data. In addition to assessing the accuracy of the final emission estimation, we also categorize error sources and discuss their relative importance. Finally, we provide recommendations for designers of future carbon footprint estimators. Experimental results support the notion that transportation mode classifiers used for carbon footprint estimation should be evaluated based on their ability to identify carbon emitting transportation modes, while giving lower priority to recognition of various stationary activities and low-emission transportation modes. Additionally, it is demonstrated that errors in the estimated traveled distance have a low impact on the overall emissions error compared to errors in the transportation mode classification or in the assumed emissions per traveled distance for a specific mode. |
| first_indexed | 2024-03-13T06:39:09Z |
| format | Article |
| id | doaj.art-7b173e0534ad48da8e3f222c160c508d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:39:09Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7b173e0534ad48da8e3f222c160c508d2023-06-08T23:00:31ZengIEEEIEEE Access2169-35362023-01-0111547825479410.1109/ACCESS.2023.328130710138385Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode ClassificationOrla Brimacombe0https://orcid.org/0009-0006-7579-4679Luis C. Gonzalez1https://orcid.org/0000-0003-1546-9752Johan Wahlstrom2https://orcid.org/0000-0003-2058-0834LiveRamp, London, U.KFaculty of Engineering, Universidad Autónoma de Chihuahua, Chihuahua City, MexicoDepartment of Computer Science, University of Exeter, Exeter, U.KAs a first step towards decreasing greenhouse gas emissions originating from transportation, it is critical that we create efficient systems for monitoring individual travel patterns and the associated carbon footprints. To this end, this paper presents a CO<sub>2</sub>e emission estimator that combines transportation mode classification with mode-specific emissions data. In addition to assessing the accuracy of the final emission estimation, we also categorize error sources and discuss their relative importance. Finally, we provide recommendations for designers of future carbon footprint estimators. Experimental results support the notion that transportation mode classifiers used for carbon footprint estimation should be evaluated based on their ability to identify carbon emitting transportation modes, while giving lower priority to recognition of various stationary activities and low-emission transportation modes. Additionally, it is demonstrated that errors in the estimated traveled distance have a low impact on the overall emissions error compared to errors in the transportation mode classification or in the assumed emissions per traveled distance for a specific mode.https://ieeexplore.ieee.org/document/10138385/Transportation mode classificationcarbon footprint estimation |
| spellingShingle | Orla Brimacombe Luis C. Gonzalez Johan Wahlstrom Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification IEEE Access Transportation mode classification carbon footprint estimation |
| title | Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification |
| title_full | Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification |
| title_fullStr | Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification |
| title_full_unstemmed | Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification |
| title_short | Smartphone-Based CO<sub>2</sub>e Emission Estimation Using Transportation Mode Classification |
| title_sort | smartphone based co sub 2 sub e emission estimation using transportation mode classification |
| topic | Transportation mode classification carbon footprint estimation |
| url | https://ieeexplore.ieee.org/document/10138385/ |
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