An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India
Fossil fuels cause air pollution and climate change, impacting human health. Mumbai imports and spends heavily on petroleum. Therefore, to reduce the amount of fossil fuel and for mitigating environmental issues, the use of electric vehicles (EVs) is an effective solution. The first priority towards...
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my.uniten.dspace-339472024-10-14T11:17:29Z An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India Rane N.L. Achari A. Saha A. Poddar I. Rane J. Pande C.B. Roy R. 57219453239 58165906700 57222261542 58713368400 58316635400 57193547008 57222031738 Air pollution Charging infrastructure EVs Geographic information system Multi-criteria decision-making Site selection India Maharashtra Mumbai Air pollution Charging (batteries) Climate change Decision making Electric vehicles Fossil fuels Information systems Information use Site selection Charging infrastructures Charging station Electric vehicle charging Factor weight Geographical information Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Suitability index Technique for order preference by similarities to ideal solutions atmospheric pollution decision making electric vehicle GIS site selection technology adoption transportation infrastructure Geographic information systems Fossil fuels cause air pollution and climate change, impacting human health. Mumbai imports and spends heavily on petroleum. Therefore, to reduce the amount of fossil fuel and for mitigating environmental issues, the use of electric vehicles (EVs) is an effective solution. The first priority towards supporting the widespread adoption of EVs is the availability of convenient charging stations. This research aims to delineate the optimal places for new electric vehicle charging stations (EVCS) in study area. The interrelationship of thirteen parameters have been used to determine the Multi Influencing Factor (MIF) weights. These MIF weights were then integrated into Geographical Information System (GIS) for weighted overlay analysis. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) used to assign ranks based on suitability index values. The result shows that the zone falling between 297.587 to 488.520 suitability index has suitability for EVCS. The proposed methodology offers a more precise solution for EVCS problems with a high level of uncertainty and assists policymakers and administrators in making effective decisions for future planning and strategies. � 2023 Elsevier Ltd Final 2024-10-14T03:17:29Z 2024-10-14T03:17:29Z 2023 Article 10.1016/j.scs.2023.104717 2-s2.0-85162174411 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162174411&doi=10.1016%2fj.scs.2023.104717&partnerID=40&md5=a5e2781272becab1c99a4218712b9c38 https://irepository.uniten.edu.my/handle/123456789/33947 97 104717 Elsevier Ltd Scopus |
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Air pollution Charging infrastructure EVs Geographic information system Multi-criteria decision-making Site selection India Maharashtra Mumbai Air pollution Charging (batteries) Climate change Decision making Electric vehicles Fossil fuels Information systems Information use Site selection Charging infrastructures Charging station Electric vehicle charging Factor weight Geographical information Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Suitability index Technique for order preference by similarities to ideal solutions atmospheric pollution decision making electric vehicle GIS site selection technology adoption transportation infrastructure Geographic information systems |
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Air pollution Charging infrastructure EVs Geographic information system Multi-criteria decision-making Site selection India Maharashtra Mumbai Air pollution Charging (batteries) Climate change Decision making Electric vehicles Fossil fuels Information systems Information use Site selection Charging infrastructures Charging station Electric vehicle charging Factor weight Geographical information Multi criteria decision-making Multicriteria decision-making Multicriterion decision makings Suitability index Technique for order preference by similarities to ideal solutions atmospheric pollution decision making electric vehicle GIS site selection technology adoption transportation infrastructure Geographic information systems Rane N.L. Achari A. Saha A. Poddar I. Rane J. Pande C.B. Roy R. An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
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Fossil fuels cause air pollution and climate change, impacting human health. Mumbai imports and spends heavily on petroleum. Therefore, to reduce the amount of fossil fuel and for mitigating environmental issues, the use of electric vehicles (EVs) is an effective solution. The first priority towards supporting the widespread adoption of EVs is the availability of convenient charging stations. This research aims to delineate the optimal places for new electric vehicle charging stations (EVCS) in study area. The interrelationship of thirteen parameters have been used to determine the Multi Influencing Factor (MIF) weights. These MIF weights were then integrated into Geographical Information System (GIS) for weighted overlay analysis. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) used to assign ranks based on suitability index values. The result shows that the zone falling between 297.587 to 488.520 suitability index has suitability for EVCS. The proposed methodology offers a more precise solution for EVCS problems with a high level of uncertainty and assists policymakers and administrators in making effective decisions for future planning and strategies. � 2023 Elsevier Ltd |
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57219453239 Rane N.L. Achari A. Saha A. Poddar I. Rane J. Pande C.B. Roy R. |
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Rane N.L. Achari A. Saha A. Poddar I. Rane J. Pande C.B. Roy R. |
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Rane N.L. |
title |
An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
title_short |
An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
title_full |
An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
title_fullStr |
An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
title_full_unstemmed |
An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India |
title_sort |
integrated gis, mif, and topsis approach for appraising electric vehicle charging station suitability zones in mumbai, india |
publisher |
Elsevier Ltd |
publishDate |
2024 |
_version_ |
1814061158971211776 |
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13.209306 |