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An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

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  • Peikun Li

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Quantao Yang

    (Department of Public Security, Shaanxi Police College, Xi’an 710021, China)

  • Wenbo Lu

    (School of Transportation, Southeast University, Nanjing 214135, China
    Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia)

  • Shu Xi

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Hao Wang

    (Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

Suggested Citation

  • Peikun Li & Quantao Yang & Wenbo Lu & Shu Xi & Hao Wang, 2024. "An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environ," Land, MDPI, vol. 13(11), pages 1-20, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1887-:d:1518579
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    References listed on IDEAS

    as
    1. Zhenbao Wang & Shuyue Liu & Haitao Lian & Xinyi Chen, 2024. "Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach," Land, MDPI, vol. 13(8), pages 1-16, August.
    2. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    3. Yadi Zhu & Feng Chen & Zijia Wang & Jin Deng, 2019. "Spatio-temporal analysis of rail station ridership determinants in the built environment," Transportation, Springer, vol. 46(6), pages 2269-2289, December.
    4. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    5. Peikun Li & Quantao Yang & Wenbo Lu, 2024. "Nonlinear Relationship of Multi-Source Land Use Features with Temporal Travel Distances at Subway Station Level: Empirical Study from Xi’an City," Land, MDPI, vol. 13(7), pages 1-16, July.
    6. Shaoying Li & Xiaoping Liu & Zhigang Li & Zhifeng Wu & Zijun Yan & Yimin Chen & Feng Gao, 2018. "Spatial and Temporal Dynamics of Urban Expansion along the Guangzhou–Foshan Inter-City Rail Transit Corridor, China," Sustainability, MDPI, vol. 10(3), pages 1-18, February.
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