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Exploring the Impact of Built Environment on Elderly Metro Ridership at Station-to-Station Level

Author

Listed:
  • Chaojie Duan

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Shuhong Ma

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Chenguang Li

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

Abstract

Understanding the relationship between the built environment and metro ridership has become essential for advancing sustainable transportation development. Limited research has been given to how built environment factors influence metro ridership at a station-to-station level. Moreover, most studies focus on the general population, overlooking the special groups. This study examines the influence of the built environment on metro origin–destination (OD) ridership for older adults. Specially, we employ the CatBoost model, along with SHAP interpretation, to assess feature importance and capture nonlinear effects. Taking Xi’an as a case study, the results show that: (1) The CatBoost model demonstrates superior fitting and predictive performance, outperforming both the XGBoost and Logistic Regression models. (2) There are distinct variations in the influence of built environment factors at origin and destination stations. Traffic-related variables have a stronger effect at origin stations, while land-use variables exert a more significant influence at destination stations. (3) The built environment’s impact on older adults’ metro ridership exhibits a clear nonlinear relationship. Notably, an optimal land-use mix of 1.8–1.9 and a job density of 5000–7000 significantly enhance ridership. These findings provide valuable insights into how the built environment shapes older adults’ metro travel, contributing to the sustainable urban development.

Suggested Citation

  • Chaojie Duan & Shuhong Ma & Chenguang Li, 2024. "Exploring the Impact of Built Environment on Elderly Metro Ridership at Station-to-Station Level," Sustainability, MDPI, vol. 16(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10302-:d:1528743
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    References listed on IDEAS

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    1. Ming Chen & Ting Wang & Zongshi Liu & Ye Li & Meiting Tu, 2024. "Nonlinear and Threshold Effects of the Built Environment on Dockless Bike-Sharing," Sustainability, MDPI, vol. 16(17), pages 1-18, September.
    2. Hong Leng & Bingbing Han, 2022. "Effect of Environmental Planning on Elderly Individual Quality of Life in Severe Cold Regions: A Case Study in Northeastern China," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    3. Chenguang Li & Duo Wang & Hong Chen & Enze Liu, 2024. "Analysis of Urban Congestion Traceability: The Role of the Built Environment," Land, MDPI, vol. 13(2), pages 1-15, February.
    4. Tao Zhang & Yibo Yan & Qi Chen & Ze Liu, 2022. "Evaluation Method of Composite Development Bus Terminal Using Multi-Source Data Processing," Land, MDPI, vol. 11(10), pages 1-14, October.
    5. Wenxiao Wang & Yi Zhang & Chunli Zhao & Xiaofei Liu & Xumei Chen & Chaoyang Li & Tao Wang & Jiani Wu & Lanjing Wang, 2021. "Nonlinear Associations of the Built Environment with Cycling Frequency among Older Adults in Zhongshan, China," IJERPH, MDPI, vol. 18(20), pages 1-19, October.
    6. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    7. Cheng, Long & Wang, Kailai & De Vos, Jonas & Huang, Jie & Witlox, Frank, 2022. "Exploring non-linear built environment effects on the integration of free-floating bike-share and urban rail transport: A quantile regression approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 175-187.
    8. Qi Chen & Yibo Yan & Xu Zhang & Jian Chen, 2022. "Impact of Subjective and Objective Factors on Subway Travel Behavior: Spatial Differentiation," IJERPH, MDPI, vol. 19(23), pages 1-17, November.
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