Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area
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 p...
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MDPI AG
2025-05-01
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| author | Xiang Li Fa Zhang Ziyi Liu Yao Wei Runlong Dai Zhiyue Qiu Yuxin Gu Hong Yuan |
| author_facet | Xiang Li Fa Zhang Ziyi Liu Yao Wei Runlong Dai Zhiyue Qiu Yuxin Gu Hong Yuan |
| author_sort | Xiang Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7192b48c19cf4ad6a2aedeb51f36b7a0 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-7192b48c19cf4ad6a2aedeb51f36b7a02025-08-20T01:56:28ZengMDPI AGLand2073-445X2025-05-01145103910.3390/land14051039Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station AreaXiang Li0Fa Zhang1Ziyi Liu2Yao Wei3Runlong Dai4Zhiyue Qiu5Yuxin Gu6Hong Yuan7School of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Architecture, Southwest Jiaotong University, Chengdu 611756, ChinaThe 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.https://www.mdpi.com/2073-445X/14/5/1039high-speed railway (HSR)spatial decision support systems (SDSSs)geographic information systems (GISs)station areamachine learningland-use planning |
| spellingShingle | Xiang Li Fa Zhang Ziyi Liu Yao Wei Runlong Dai Zhiyue Qiu Yuxin Gu Hong Yuan Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area Land high-speed railway (HSR) spatial decision support systems (SDSSs) geographic information systems (GISs) station area machine learning land-use planning |
| title | Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area |
| title_full | Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area |
| title_fullStr | Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area |
| title_full_unstemmed | Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area |
| title_short | Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area |
| title_sort | machine learning driven multimodal feature extraction and optimization strategies for high speed railway station area |
| topic | high-speed railway (HSR) spatial decision support systems (SDSSs) geographic information systems (GISs) station area machine learning land-use planning |
| url | https://www.mdpi.com/2073-445X/14/5/1039 |
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