Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area

Xiang Li, Fa Zhang, Ziyi Liu, Yao Wei, Runlong Dai, Zhiyue Qiu, Yuxin Gu and Hong Yuan ()
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Xiang Li: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Fa Zhang: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Ziyi Liu: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Yao Wei: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Runlong Dai: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Zhiyue Qiu: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Yuxin Gu: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China
Hong Yuan: School of Architecture, Southwest Jiaotong University, Chengdu 611756, China

Land, 2025, vol. 14, issue 5, 1-29

Abstract: The construction of high-speed railway (HSR) station areas serves as a crucial catalyst for urban spatial evolution. However, the absence of targeted urban management theories has led to widespread spatial resource waste and post-construction abandonment phenomena in these areas. Existing research predominantly focuses on development strategies for individual construction elements of HSR stations yet lacks comprehensive strategy formulation through coordinated multi-level elements from a sustainable perspective. This study establishes a national database comprising 1018 HSR station area samples across China in 2020, integrating built environment characteristics, HSR network topology, ecological considerations, and socioeconomic indicators. Guided by the land equilibrium utilization theory, we employ the random forest Boruta algorithm to identify critical features, using land supply capacity and development intensity as target variables. Subsequently, K-means++ clustering analysis based on these key variables categorizes the samples into nine distinct clusters. Through normal distribution tests, we establish reference ranges for cluster-specific indicators and propose tailored development strategies across multiple dimensions. This research develops a multimodal feature extraction and evaluation framework specifically designed for the large-scale analysis of HSR station areas. The nine-category strategic recommendations with defined quantitative threshold intervals provide decision-makers with visually intuitive, operationally implementable, and practically significant guidance for spatial planning and resource allocation.

Keywords: high-speed railway (HSR); spatial decision support systems (SDSSs); geographic information systems (GISs); station area; machine learning; land-use planning (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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