{"title":"利用邻近图优化电动汽车充电站的位置","authors":"","doi":"10.1016/j.scs.2024.105719","DOIUrl":null,"url":null,"abstract":"<div><p>The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.</p></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2210670724005444/pdfft?md5=65488eb4f189d074ddf29b9dd0a729f7&pid=1-s2.0-S2210670724005444-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Optimal location of electric vehicle charging stations using proximity diagrams\",\"authors\":\"\",\"doi\":\"10.1016/j.scs.2024.105719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.</p></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2210670724005444/pdfft?md5=65488eb4f189d074ddf29b9dd0a729f7&pid=1-s2.0-S2210670724005444-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670724005444\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724005444","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Optimal location of electric vehicle charging stations using proximity diagrams
The concern over the planet environmental crisis is propelling administrations to promote anti-pollution regulations progressively restricting the use of fossil fuels. In this context, the integration of electric vehicles is increasingly being explored to reduce traffic emissions. To facilitate the transition from conventional vehicles to electrical ones, establishment of a robust charging infrastructure is essential. This article presents a numerical proposal for identifying potential optimal locations of additional electrical charging stations building upon the current infrastructure. For this purpose, Voronoi diagrams, constructed using the existing charging stations as point generators of the structure, will be stated as the primary framework. The extracted proximity data, delimited by geographical, legislative or user behavior-related factors, facilitates the identification of locations where companies in the sector are inclined to invest. This procedure holds significant relevance for industry in the sector seeking strategic investment locations that minimize competition while ensuring a high level of convenience for long-distance electrical vehicles users. The significance of this modeling lies in its simplicity and applicability to any region worldwide, making it a versatile tool for similar studies.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;