Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning
As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the expe...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/14/2/416 |
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| author | Mingyi Kuang Fei Fu Fangzhou Tian Liwei Lin Can Du Yuesong Zhang |
| author_facet | Mingyi Kuang Fei Fu Fangzhou Tian Liwei Lin Can Du Yuesong Zhang |
| author_sort | Mingyi Kuang |
| collection | DOAJ |
| description | As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities. |
| format | Article |
| id | doaj-art-86928ee48c274757bca2d4b6fb39b1f8 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-86928ee48c274757bca2d4b6fb39b1f82025-08-20T02:03:28ZengMDPI AGLand2073-445X2025-02-0114241610.3390/land14020416Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine LearningMingyi Kuang0Fei Fu1Fangzhou Tian2Liwei Lin3Can Du4Yuesong Zhang5School of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaChengdu Planning and Natural Resources Bureau, Chengdu 610042, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaAs urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities.https://www.mdpi.com/2073-445X/14/2/416metro station spatial growth predictionblock-level land use patternplot ratio controlmachine learning algorithmenhanced Markov–PLUS model |
| spellingShingle | Mingyi Kuang Fei Fu Fangzhou Tian Liwei Lin Can Du Yuesong Zhang Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning Land metro station spatial growth prediction block-level land use pattern plot ratio control machine learning algorithm enhanced Markov–PLUS model |
| title | Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning |
| title_full | Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning |
| title_fullStr | Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning |
| title_full_unstemmed | Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning |
| title_short | Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning |
| title_sort | study on subway station street block level land use pattern and plot ratio control based on machine learning |
| topic | metro station spatial growth prediction block-level land use pattern plot ratio control machine learning algorithm enhanced Markov–PLUS model |
| url | https://www.mdpi.com/2073-445X/14/2/416 |
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