Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning

Mingyi Kuang, Fei Fu (), Fangzhou Tian, Liwei Lin, Can Du and Yuesong Zhang
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Mingyi Kuang: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Fei Fu: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Fangzhou Tian: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Liwei Lin: Chengdu Planning and Natural Resources Bureau, Chengdu 610042, China
Can Du: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Yuesong Zhang: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China

Land, 2025, vol. 14, issue 2, 1-19

Abstract: 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.

Keywords: metro station spatial growth prediction; block-level land use pattern; plot ratio control; machine learning algorithm; enhanced Markov–PLUS model (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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