Scholarlink

<p>Understanding metro ridership drivers is vital for transit planning. This study employs a multidimensional framework–encompassing the built environment, intermodal interaction, network topology, and operational features–to analyze ridership at 100 Guangzhou metro stations, focusing on two peak days: 31 December 2024 (12.2 million riders) and 1 January 2025 (national record). Three models (OLS, XGBoost, LightGBM) are tested, with LightGBM selected. SHAP is used to interpret the model and identify key variables. Results show that operational features, especially the capacity coefficient (CO), exert the greatest influence, surpassing traditional factors. Other significant predictors include commercial area (CA), parking lots (PL), and betweenness centrality (BC). The study also introduces a novel application of Line 11 as a spatial boundary for defining the downtown area. These findings provide actionable insights for enhancing metro operations, capacity, and multimodal integration, demonstrating the utility of machine learning and SHAP-based interpretation in understanding metro ridership under extreme conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multidimensional station level determinants of metro ridership during extreme demand days in Guangzhou

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

查看全文
  • Bingjie Zhan

摘要

Understanding metro ridership drivers is vital for transit planning. This study employs a multidimensional framework–encompassing the built environment, intermodal interaction, network topology, and operational features–to analyze ridership at 100 Guangzhou metro stations, focusing on two peak days: 31 December 2024 (12.2 million riders) and 1 January 2025 (national record). Three models (OLS, XGBoost, LightGBM) are tested, with LightGBM selected. SHAP is used to interpret the model and identify key variables. Results show that operational features, especially the capacity coefficient (CO), exert the greatest influence, surpassing traditional factors. Other significant predictors include commercial area (CA), parking lots (PL), and betweenness centrality (BC). The study also introduces a novel application of Line 11 as a spatial boundary for defining the downtown area. These findings provide actionable insights for enhancing metro operations, capacity, and multimodal integration, demonstrating the utility of machine learning and SHAP-based interpretation in understanding metro ridership under extreme conditions.