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<p>Conventional subway analyses relying on static IC card data or limited surveys fail to capture the complex, dynamic interactions between passenger flows, land use patterns, and multimodal transportation choices in high-density urban environments. This study overcomes these methodological limitations by developing an innovative analytical framework that synergistically integrates large-scale mobile phone positioning data with traditional transport surveys and automated fare collection records. Using Guangzhou’s extensive metro system (247 stations across 531&#xa0;km) as a representative case study, we employ advanced data fusion techniques and machine learning algorithms to reconstruct complete travel chains and dynamically delineate station service areas with unprecedented spatial-temporal resolution. Our hybrid methodology combines multinomial logit modeling with random forest classification to systematically quantify subway competitiveness across different urban contexts, revealing three key findings: First, we identify distinct spatial thresholds for effective service areas (800&#xa0;m radius in central business districts vs. 1.4&#xa0;km in suburban corridors). Second, the analysis uncovers an inverted U-shaped association between parking supply-demand ratios and mode share optimal balance at 1.75. Finally, and most critically from an equity perspective, the significant disparities suggesting that low-income suburban commuters experience are associated with 2 times higher spatiotemporal costs than central city residents. The proposed framework provides urban planners with a robust, scalable tool for transit network optimization, offering particular value for rapidly urbanizing megacities in Asia and other developing regions. By effectively bridging cutting-edge big data analytics with established transportation modeling approaches, the proposed framework provides a robust tool for transit optimization. Ultimately, this study answers critical questions regarding the performance and equity of subway systems in high-density environments, bridging cutting-edge big data analytics with established transportation modeling approaches.</p>

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A mobile data-enhanced framework for spatial-temporal analysis of subway competitiveness and equity implications

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Caixia Li,
  • Cong Cong,
  • Hunan Deng,
  • Jiachao Chen,
  • Junhui Li

摘要

Conventional subway analyses relying on static IC card data or limited surveys fail to capture the complex, dynamic interactions between passenger flows, land use patterns, and multimodal transportation choices in high-density urban environments. This study overcomes these methodological limitations by developing an innovative analytical framework that synergistically integrates large-scale mobile phone positioning data with traditional transport surveys and automated fare collection records. Using Guangzhou’s extensive metro system (247 stations across 531 km) as a representative case study, we employ advanced data fusion techniques and machine learning algorithms to reconstruct complete travel chains and dynamically delineate station service areas with unprecedented spatial-temporal resolution. Our hybrid methodology combines multinomial logit modeling with random forest classification to systematically quantify subway competitiveness across different urban contexts, revealing three key findings: First, we identify distinct spatial thresholds for effective service areas (800 m radius in central business districts vs. 1.4 km in suburban corridors). Second, the analysis uncovers an inverted U-shaped association between parking supply-demand ratios and mode share optimal balance at 1.75. Finally, and most critically from an equity perspective, the significant disparities suggesting that low-income suburban commuters experience are associated with 2 times higher spatiotemporal costs than central city residents. The proposed framework provides urban planners with a robust, scalable tool for transit network optimization, offering particular value for rapidly urbanizing megacities in Asia and other developing regions. By effectively bridging cutting-edge big data analytics with established transportation modeling approaches, the proposed framework provides a robust tool for transit optimization. Ultimately, this study answers critical questions regarding the performance and equity of subway systems in high-density environments, bridging cutting-edge big data analytics with established transportation modeling approaches.