Research on Resource Allocation Optimization of Smart City Based on Big Data
The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing proble...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9171303/ |
| _version_ | 1818853258885595136 |
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| author | Junling Zhou Pohsun Wang Lingfeng Xie |
| author_facet | Junling Zhou Pohsun Wang Lingfeng Xie |
| author_sort | Junling Zhou |
| collection | DOAJ |
| description | The resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation. |
| first_indexed | 2024-12-19T07:33:58Z |
| format | Article |
| id | doaj.art-e4f2bf7d4e1742f0802adcd3e0623c6f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:33:58Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e4f2bf7d4e1742f0802adcd3e0623c6f2022-12-21T20:30:38ZengIEEEIEEE Access2169-35362020-01-01815885215886110.1109/ACCESS.2020.30177659171303Research on Resource Allocation Optimization of Smart City Based on Big DataJunling Zhou0Pohsun Wang1https://orcid.org/0000-0001-9952-2594Lingfeng Xie2School of Design, Shunde Polytechnic, Foshan, ChinaFaculty of Innovation and Design, City University of Macau, Taipa, MacauArchitectural Design and Research Institute, Guangzhou University, Guangzhou, ChinaThe resource allocation of charging stations is an important part of promoting the development of renewable energy in modern cities. It can promote the scientific and modern construction of urban resource allocation and promote the intelligent transformation of cities. In view of the existing problems in the resource allocation process of urban charging stations, such as a single planning method, considering the actual travel demand. Based on the smart city transportation network information, this article will consider the impact of charging station construction costs, user driving and waiting costs on the location of charging stations, construct a charging station configuration optimization model, and introduce charging convenience coefficients to modify the model. Secondly, this paper establishes a systematic clustering model based on principal component analysis, selecting factors such as per capita GDP, population, and civilian car ownership as indicators, clustering analysis of different regions and assigning different charging convenience coefficients. Finally, the shortest distance matrix between any two nodes is calculated by the Voronoi diagram to concentrate the regional charging load to the traffic node, and the Floyd algorithm is used to analyze and evaluate the effect of the established charging station configuration optimization model. This technology provides a basis for promoting the modernization of urban green transportation.https://ieeexplore.ieee.org/document/9171303/Smart citycharging stationconfiguration optimizationbig data |
| spellingShingle | Junling Zhou Pohsun Wang Lingfeng Xie Research on Resource Allocation Optimization of Smart City Based on Big Data IEEE Access Smart city charging station configuration optimization big data |
| title | Research on Resource Allocation Optimization of Smart City Based on Big Data |
| title_full | Research on Resource Allocation Optimization of Smart City Based on Big Data |
| title_fullStr | Research on Resource Allocation Optimization of Smart City Based on Big Data |
| title_full_unstemmed | Research on Resource Allocation Optimization of Smart City Based on Big Data |
| title_short | Research on Resource Allocation Optimization of Smart City Based on Big Data |
| title_sort | research on resource allocation optimization of smart city based on big data |
| topic | Smart city charging station configuration optimization big data |
| url | https://ieeexplore.ieee.org/document/9171303/ |
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