Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles
Europe strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | EURO Journal on Transportation and Logistics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192437623000158 |
| _version_ | 1797733081790021632 |
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| author | Selcen Gülsüm Aslan Özşahin Babek Erdebilli |
| author_facet | Selcen Gülsüm Aslan Özşahin Babek Erdebilli |
| author_sort | Selcen Gülsüm Aslan Özşahin |
| collection | DOAJ |
| description | Europe strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only if electric vehicles dominate the transportation sector. Paving the way for electric vehicle deployment on roads is subject to the provision of electric-vehicle-charging stations on the roads such that sufficiently good driving experience without any obstacles can be achieved. To address this timely societal challenge, we proposed a novel methodology by using the well-known facility-location-allocation methodology named set-covering location models with statistical machine learning and developed it for the problem settings of identifying electric-vehicle-charging station locations. Statistical machine learning was employed in the proposed model to more precisely identify and determine feasible coverage sets. We demonstrated the efficiency of the proposed model for the Capital Region of Denmark, where the green transition is part of the political agenda and is of severe societal concern, by using the newly collected main road transportation dataset. |
| first_indexed | 2024-03-12T12:24:00Z |
| format | Article |
| id | doaj.art-a96ec91589d24e5fbdf878c375c7591d |
| institution | Directory Open Access Journal |
| issn | 2192-4384 |
| language | English |
| last_indexed | 2024-03-12T12:24:00Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Transportation and Logistics |
| spelling | doaj.art-a96ec91589d24e5fbdf878c375c7591d2023-08-30T05:50:26ZengElsevierEURO Journal on Transportation and Logistics2192-43842023-01-0112100118Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehiclesSelcen Gülsüm Aslan Özşahin0Babek Erdebilli1TOBB University of Economics and Technology, Ankara, Turkiye; Corresponding author.Ankara Yildirim Beyazit University, Ankara, TurkiyeEurope strengthens its policies on climate change, green transition, and sustainable energy by addressing the high greenhouse-gas emissions in the transportation sector. Europe aims to reduce such emissions and reach a state of carbon neutrality by 2030 and 2050, respectively. This is feasible only if electric vehicles dominate the transportation sector. Paving the way for electric vehicle deployment on roads is subject to the provision of electric-vehicle-charging stations on the roads such that sufficiently good driving experience without any obstacles can be achieved. To address this timely societal challenge, we proposed a novel methodology by using the well-known facility-location-allocation methodology named set-covering location models with statistical machine learning and developed it for the problem settings of identifying electric-vehicle-charging station locations. Statistical machine learning was employed in the proposed model to more precisely identify and determine feasible coverage sets. We demonstrated the efficiency of the proposed model for the Capital Region of Denmark, where the green transition is part of the political agenda and is of severe societal concern, by using the newly collected main road transportation dataset.http://www.sciencedirect.com/science/article/pii/S2192437623000158Green transportationGreen transitionIntelligent optimizationML in SCLMML-based covering problemsStatistical-machine-learning-based intelligent optimization |
| spellingShingle | Selcen Gülsüm Aslan Özşahin Babek Erdebilli Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles EURO Journal on Transportation and Logistics Green transportation Green transition Intelligent optimization ML in SCLM ML-based covering problems Statistical-machine-learning-based intelligent optimization |
| title | Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles |
| title_full | Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles |
| title_fullStr | Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles |
| title_full_unstemmed | Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles |
| title_short | Statistical-machine-learning-based intelligent relaxation for set-covering location models to identify locations of charging stations for electric vehicles |
| title_sort | statistical machine learning based intelligent relaxation for set covering location models to identify locations of charging stations for electric vehicles |
| topic | Green transportation Green transition Intelligent optimization ML in SCLM ML-based covering problems Statistical-machine-learning-based intelligent optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2192437623000158 |
| work_keys_str_mv | AT selcengulsumaslanozsahin statisticalmachinelearningbasedintelligentrelaxationforsetcoveringlocationmodelstoidentifylocationsofchargingstationsforelectricvehicles AT babekerdebilli statisticalmachinelearningbasedintelligentrelaxationforsetcoveringlocationmodelstoidentifylocationsofchargingstationsforelectricvehicles |