Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china
Abstract Under the pressure of carbon pollution and resource scarcity, electric vehicles (EVs) have gradually replaced fuel vehicles as a new trend of low-carbon transformation. However, public charging stations (PCS) face with problems such as insufficient quantity and unreasonable distribution. By...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-91106-9 |
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| author | Qimeng Ren Ming Sun |
| author_facet | Qimeng Ren Ming Sun |
| author_sort | Qimeng Ren |
| collection | DOAJ |
| description | Abstract Under the pressure of carbon pollution and resource scarcity, electric vehicles (EVs) have gradually replaced fuel vehicles as a new trend of low-carbon transformation. However, public charging stations (PCS) face with problems such as insufficient quantity and unreasonable distribution. By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. This method makes up for the deficiencies of too single consideration factor, lack of intuitiveness of mathematical model and lack of urban geospatial research. This is of significance for predicting the demand distribution of PCS in the future and further promoting the whole popularity of EVs. |
| format | Article |
| id | doaj-art-0ece22f8e21c4f6e8a7f4511cf020ad2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-0ece22f8e21c4f6e8a7f4511cf020ad22025-08-20T03:04:20ZengNature PortfolioScientific Reports2045-23222025-02-0115111710.1038/s41598-025-91106-9Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, chinaQimeng Ren0Ming Sun1School of Landscape, Northeast Forestry UniversitySchool of Landscape, Northeast Forestry UniversityAbstract Under the pressure of carbon pollution and resource scarcity, electric vehicles (EVs) have gradually replaced fuel vehicles as a new trend of low-carbon transformation. However, public charging stations (PCS) face with problems such as insufficient quantity and unreasonable distribution. By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. This method makes up for the deficiencies of too single consideration factor, lack of intuitiveness of mathematical model and lack of urban geospatial research. This is of significance for predicting the demand distribution of PCS in the future and further promoting the whole popularity of EVs.https://doi.org/10.1038/s41598-025-91106-9Spatial demand for public charging stations (PCS)Electric vehicles (EVs)Multi-source big dataSpatial analysisGeographical information system (GIS)Entropy method |
| spellingShingle | Qimeng Ren Ming Sun Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china Scientific Reports Spatial demand for public charging stations (PCS) Electric vehicles (EVs) Multi-source big data Spatial analysis Geographical information system (GIS) Entropy method |
| title | Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china |
| title_full | Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china |
| title_fullStr | Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china |
| title_full_unstemmed | Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china |
| title_short | Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china |
| title_sort | predicting the spatial demand for public charging stations for evs using multi source big data an example from jinan city china |
| topic | Spatial demand for public charging stations (PCS) Electric vehicles (EVs) Multi-source big data Spatial analysis Geographical information system (GIS) Entropy method |
| url | https://doi.org/10.1038/s41598-025-91106-9 |
| work_keys_str_mv | AT qimengren predictingthespatialdemandforpublicchargingstationsforevsusingmultisourcebigdataanexamplefromjinancitychina AT mingsun predictingthespatialdemandforpublicchargingstationsforevsusingmultisourcebigdataanexamplefromjinancitychina |