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Multi Criteria Decision Analysis to Optimise Siting of Electric Vehicle Charging Points—Case Study Winchester District, UK

层次分析法 运输工程 工作(物理) 点(几何) 人口 地理信息系统 过程(计算) 住宅区 停车和乘车 运筹学 工程类 计算机科学 公共交通 地理 土木工程 地图学 数学 操作系统 社会学 人口学 机械工程 几何学
作者
Mostafa Mahdy,A.S. Bahaj,Philip Turner,Naomi Wise,Abdulsalam S. Alghamdi,Hidab Hamwi
出处
期刊:Energies [Multidisciplinary Digital Publishing Institute]
卷期号:15 (7): 2497-2497 被引量:31
标识
DOI:10.3390/en15072497
摘要

Achieving net-zero carbon in the UK by 2050 will necessitate the decarbonisation of the transportation systems. However, there are challenges to this, especially for vehicles in cities where the charging infrastructure is at its minimum. Overcoming these challenges will undoubtedly encourage electrical vehicle (EV) use, with commensurate reductions in emission coupled with better environmental conditions in cities, e.g., air quality. Drivers, on the whole, are reluctant to invest in an EV if they cannot access a convenient charging point within their living area. This research provides a methodology to support the planning for the optimum siting of charging infrastructure, so it is accessible to as many citizens as possible within a city. The work focuses on Winchester City and District in the UK. The multi-criteria decision approach is based on the Analytical Hierarchy Process (AHP) linked to site spatial assessment using Geographical Information System (GIS). The assessment considered key criteria such as road type, road access, on-road parking availability, road slope, proximity to fuel stations, current/planned charging points, car parks and population distributions. The process contains two suitability filters, namely, restricted road and suitability mask. In the first, all restricted roads were excluded from further analysis, which resulted in reducing the road segments from over 9000 to around 2000. When applying the second filter an overall result of 44 suitable EV charging point locations was achieved. These locations were validated using the Google Earth® imaging platform to check actual locations against those predicted by the analysis. The presented methodology is accurate and is generalisable to other cities or regions.

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