A hybrid suitability mapping model integrating GIS, machine learning, and multi-criteria decision analytics for optimizing service quality of electric vehicle charging stations

Elomiya, Akram; Křupka, Jiří; Jovčić, Stefan; Simic, Vladimir; Švadlenka, Libor; Pamucar, Dragan

doi:10.1016/j.scs.2024.105397

Sustainable Cities and Society

2024

DOI: 10.1016/j.scs.2024.105397

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A hybrid suitability mapping model integrating GIS, machine learning, and multi-criteria decision analytics for optimizing service quality of electric vehicle charging stations

Akram Elomiya,

Jiří Křupka,

Stefan Jovčić

et al.

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Cited by 3 publications

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Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Rabie,

Asghari,

Nosrati

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Sustainable Cities and Society

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Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Rabie,

Asghari,

Nosrati

et al. 2024

Sustainable Cities and Society

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Sustainable propulsion technology selection in penultimate mile delivery using the FullEX-AROMAN method

Bošković,

Švadlenka,

Jovčić

et al. 2024

Socio-Economic Planning Sciences

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Categorization of Attributes and Features for the Location of Electric Vehicle Charging Stations

Mazza,

Russo,

Chicco

et al. 2024

Energies

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The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure.

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A hybrid suitability mapping model integrating GIS, machine learning, and multi-criteria decision analytics for optimizing service quality of electric vehicle charging stations

Cited by 3 publications

References 50 publications

Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Spatially resolved air quality index prediction in megacities with a CNN-Bi-LSTM hybrid framework

Sustainable propulsion technology selection in penultimate mile delivery using the FullEX-AROMAN method

Categorization of Attributes and Features for the Location of Electric Vehicle Charging Stations

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