揭示城市规模的城市路边充电桩容量：地理空间知识辅助小目标检测和可持续发展目标7驱动规划

IF 12 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2025-09-03 DOI:10.1016/j.scs.2025.106789

Qin Huang , Teng Zhong , Liangchen Zhou , Rui Zhu , Xiao Fu , Changchang Zhou , Min Chen , Guonian Lü

{"title":"揭示城市规模的城市路边充电桩容量：地理空间知识辅助小目标检测和可持续发展目标7驱动规划","authors":"Qin Huang , Teng Zhong , Liangchen Zhou , Rui Zhu , Xiao Fu , Changchang Zhou , Min Chen , Guonian Lü","doi":"10.1016/j.scs.2025.106789","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid rise of electric vehicles (EVs) requires efficient detection and planning of urban roadside charging piles (RCPs) to support sustainable urban management. This study proposes a novel framework to optimize urban RCPs, integrating geospatial knowledge-assisted small object detection and Sustainable Development Goal 7 (SDG 7)-driven planning. We developed RCPs-YOLO, a tailored model that leverages geospatial knowledge to improve small object detection, achieving 89.8 % precision and 77.4 % [email protected] in detecting RCPs from street view images, and a multi-line-of-sight method for precise geographic localization. Based on the EVs roadside charging demand across Nanjing Central Districts (NCDs) in year 2024, we suggest that the RCPs could support up to 301,537 kWh/day in NCDs. We develop four SDG 7-driven planning scenarios, including business-as-usual, equity-oriented, efficiency-oriented, and balanced development. Under these scenarios, the potential annual roadside charging capacity in NCDs by 2030 is approximately 85.8 GWh, 153.5 GWh, 103.2 GWh, and 148.3 GWh, respectively. Our findings suggest prioritizing the development of RCPs in newly developed downtown areas to promote equitable access and enhance energy efficiency. This approach offers a scalable, data-driven solution for urban planners aiming to advance progress toward SDG 7 and the development of smart cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"132 ","pages":"Article 106789"},"PeriodicalIF":12.0000,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling city-scale urban roadside charging piles capacity: Geospatial knowledge-assisted small object detection and SDG 7-driven planning\",\"authors\":\"Qin Huang , Teng Zhong , Liangchen Zhou , Rui Zhu , Xiao Fu , Changchang Zhou , Min Chen , Guonian Lü\",\"doi\":\"10.1016/j.scs.2025.106789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rapid rise of electric vehicles (EVs) requires efficient detection and planning of urban roadside charging piles (RCPs) to support sustainable urban management. This study proposes a novel framework to optimize urban RCPs, integrating geospatial knowledge-assisted small object detection and Sustainable Development Goal 7 (SDG 7)-driven planning. We developed RCPs-YOLO, a tailored model that leverages geospatial knowledge to improve small object detection, achieving 89.8 % precision and 77.4 % [email protected] in detecting RCPs from street view images, and a multi-line-of-sight method for precise geographic localization. Based on the EVs roadside charging demand across Nanjing Central Districts (NCDs) in year 2024, we suggest that the RCPs could support up to 301,537 kWh/day in NCDs. We develop four SDG 7-driven planning scenarios, including business-as-usual, equity-oriented, efficiency-oriented, and balanced development. Under these scenarios, the potential annual roadside charging capacity in NCDs by 2030 is approximately 85.8 GWh, 153.5 GWh, 103.2 GWh, and 148.3 GWh, respectively. Our findings suggest prioritizing the development of RCPs in newly developed downtown areas to promote equitable access and enhance energy efficiency. This approach offers a scalable, data-driven solution for urban planners aiming to advance progress toward SDG 7 and the development of smart cities.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"132 \",\"pages\":\"Article 106789\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2025-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670725006638\",\"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/S2210670725006638","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}

引用次数: 0

摘要

随着电动汽车的快速发展，需要对城市路边充电桩进行有效的检测和规划，以支持可持续的城市管理。本研究提出了一个优化城市rcp的新框架，将地理空间知识辅助的小目标检测与可持续发展目标7驱动的规划相结合。我们开发了rcp - yolo，这是一种利用地理空间知识改进小目标检测的定制模型，从街景图像中检测rcp的准确率达到89.8%和77.4%，并采用多视距方法进行精确地理定位。基于2024年南京中心区电动汽车路边充电需求，建议RCPs可支持301537千瓦时/天的路边充电。我们制定了四个可持续发展目标7驱动的规划情景，包括一切照旧、以公平为导向、以效率为导向和平衡发展。在这些情景下，到2030年，非传染性疾病的潜在年路边充电容量分别约为85.8 GWh、153.5 GWh、103.2 GWh和148.3 GWh。我们的研究结果建议在新开发的市中心地区优先发展rcp，以促进公平获取和提高能源效率。这种方法为城市规划者提供了一种可扩展的、数据驱动的解决方案，旨在推进可持续发展目标7和智慧城市的发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Unveiling city-scale urban roadside charging piles capacity: Geospatial knowledge-assisted small object detection and SDG 7-driven planning

The rapid rise of electric vehicles (EVs) requires efficient detection and planning of urban roadside charging piles (RCPs) to support sustainable urban management. This study proposes a novel framework to optimize urban RCPs, integrating geospatial knowledge-assisted small object detection and Sustainable Development Goal 7 (SDG 7)-driven planning. We developed RCPs-YOLO, a tailored model that leverages geospatial knowledge to improve small object detection, achieving 89.8 % precision and 77.4 % [email protected] in detecting RCPs from street view images, and a multi-line-of-sight method for precise geographic localization. Based on the EVs roadside charging demand across Nanjing Central Districts (NCDs) in year 2024, we suggest that the RCPs could support up to 301,537 kWh/day in NCDs. We develop four SDG 7-driven planning scenarios, including business-as-usual, equity-oriented, efficiency-oriented, and balanced development. Under these scenarios, the potential annual roadside charging capacity in NCDs by 2030 is approximately 85.8 GWh, 153.5 GWh, 103.2 GWh, and 148.3 GWh, respectively. Our findings suggest prioritizing the development of RCPs in newly developed downtown areas to promote equitable access and enhance energy efficiency. This approach offers a scalable, data-driven solution for urban planners aiming to advance progress toward SDG 7 and the development of smart cities.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

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;