Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making
In today’s world, the heavy reliance on crude oil for transportation has significant environmental implications, contributing to air pollution and greenhouse gas emissions, exacerbating global climate change and posing health risks. As environmental sustainability concerns grow, there is a need to e...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-07-01
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| Series: | Green Technologies and Sustainability |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949736124000903 |
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| author | Mohd Mobasshir Praveen Pachauri Pratibha Kumari Faisal Khan Azhar Equbal Osama Khan Mohd Parvez Taufique Ahamad Shadab Ahmad |
| author_facet | Mohd Mobasshir Praveen Pachauri Pratibha Kumari Faisal Khan Azhar Equbal Osama Khan Mohd Parvez Taufique Ahamad Shadab Ahmad |
| author_sort | Mohd Mobasshir |
| collection | DOAJ |
| description | In today’s world, the heavy reliance on crude oil for transportation has significant environmental implications, contributing to air pollution and greenhouse gas emissions, exacerbating global climate change and posing health risks. As environmental sustainability concerns grow, there is a need to explore alternative fuel options and vehicle technologies with reduced emissions. In this study, a comparative analysis is conducted on three distinct vehicle types — hybrid, diesel, and biodiesel — assessing their carbon footprint based on emissions of CO2, CO, NOx, SO2, PM and UBHC across various operating conditions such as load, efficiency losses, and torque. The Analytic hierarchy process (AHP) method was used to determine the weights for various output parameters, including, the weights assigned to these parameters are as follows: CO2 emissions-13.76%, CO emissions-18.29%, UBHC emissions-25.58%, NOx emissions-13.54%, PM emissions-7.01%, and SO2 emissions-21.83%. Various vehicle types were ranked using the Evaluation Based on Distance from Average Solution (EDAS) approach. The experimental findings show that, out of the three vehicle types, hybrid vehicles had the best emissions profile, with lower levels of all assessed pollutants. Consequently, hybrid vehicles are identified as having the lowest carbon footprint, followed by diesel vehicles, with biodiesel vehicles exhibiting the highest emissions. K-means clustering is used to determine which type of vehicle is most effective at reducing emissions. With emissions of 95 g/km, 0.2 g/km, 0.015 g/km, 0.02 g/km, 0.001 g/km, and 0.005 g/km for CO2, CO, UBHC, PM, and SO2, the hybrid car in cluster 1 yields the most promising results. This study underscores the importance of considering environmental impacts in vehicle selection and highlights the potential of hybrid technology in mitigating carbon emissions, highlighted by an insightful K-means clustering study. |
| format | Article |
| id | doaj-art-7120065675234d20aec3ae7b364825db |
| institution | Kabale University |
| issn | 2949-7361 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Green Technologies and Sustainability |
| spelling | doaj-art-7120065675234d20aec3ae7b364825db2025-01-03T04:09:04ZengKeAi Communications Co., Ltd.Green Technologies and Sustainability2949-73612025-07-0133100163Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-makingMohd Mobasshir0Praveen Pachauri1Pratibha Kumari2Faisal Khan3Azhar Equbal4Osama Khan5Mohd Parvez6Taufique Ahamad7Shadab Ahmad8Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, IndiaGovernment Polytechnic Siwan, Bihar, UP, IndiaDepartment of Mechanical Engineering, KIET Group of Institutions, Ghaziabad (UP), 201206, IndiaDepartment of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, India; Corresponding authors.Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, India; Corresponding authors.Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, India; Corresponding authors.Department of Mechanical Engineering, Al-Falah University, Faridabad, Haryana 121004, IndiaDepartment of Mechanical Engineering, Al-Falah University, Faridabad, Haryana 121004, IndiaDepartment of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, IndiaIn today’s world, the heavy reliance on crude oil for transportation has significant environmental implications, contributing to air pollution and greenhouse gas emissions, exacerbating global climate change and posing health risks. As environmental sustainability concerns grow, there is a need to explore alternative fuel options and vehicle technologies with reduced emissions. In this study, a comparative analysis is conducted on three distinct vehicle types — hybrid, diesel, and biodiesel — assessing their carbon footprint based on emissions of CO2, CO, NOx, SO2, PM and UBHC across various operating conditions such as load, efficiency losses, and torque. The Analytic hierarchy process (AHP) method was used to determine the weights for various output parameters, including, the weights assigned to these parameters are as follows: CO2 emissions-13.76%, CO emissions-18.29%, UBHC emissions-25.58%, NOx emissions-13.54%, PM emissions-7.01%, and SO2 emissions-21.83%. Various vehicle types were ranked using the Evaluation Based on Distance from Average Solution (EDAS) approach. The experimental findings show that, out of the three vehicle types, hybrid vehicles had the best emissions profile, with lower levels of all assessed pollutants. Consequently, hybrid vehicles are identified as having the lowest carbon footprint, followed by diesel vehicles, with biodiesel vehicles exhibiting the highest emissions. K-means clustering is used to determine which type of vehicle is most effective at reducing emissions. With emissions of 95 g/km, 0.2 g/km, 0.015 g/km, 0.02 g/km, 0.001 g/km, and 0.005 g/km for CO2, CO, UBHC, PM, and SO2, the hybrid car in cluster 1 yields the most promising results. This study underscores the importance of considering environmental impacts in vehicle selection and highlights the potential of hybrid technology in mitigating carbon emissions, highlighted by an insightful K-means clustering study.http://www.sciencedirect.com/science/article/pii/S2949736124000903BiodieselDiesel engineCarbon FootprintHybrid vehicleEnergy efficiencyMachine learning |
| spellingShingle | Mohd Mobasshir Praveen Pachauri Pratibha Kumari Faisal Khan Azhar Equbal Osama Khan Mohd Parvez Taufique Ahamad Shadab Ahmad Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making Green Technologies and Sustainability Biodiesel Diesel engine Carbon Footprint Hybrid vehicle Energy efficiency Machine learning |
| title | Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making |
| title_full | Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making |
| title_fullStr | Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making |
| title_full_unstemmed | Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making |
| title_short | Analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k-means clustering for sustainable transportation decision-making |
| title_sort | analyzing vehicle emissions using a hybrid machine learning approach using weighted average based k means clustering for sustainable transportation decision making |
| topic | Biodiesel Diesel engine Carbon Footprint Hybrid vehicle Energy efficiency Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2949736124000903 |
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