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Using metro smart card data to model location choice of after-work activities: An application to Shanghai

Citations

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Cited by:

  1. Jiao, Hongzan & Huang, Shibiao & Zhou, Yu, 2023. "Understanding the land use function of station areas based on spatiotemporal similarity in rail transit ridership: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 109(C).
  2. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
  3. Bi, Hui & Ye, Zhirui & Zhao, Jiahui & Chen, Enhui, 2020. "Real trip costs: Modelling intangible costs of urban online car-hailing in Haikou," Transport Policy, Elsevier, vol. 96(C), pages 128-140.
  4. Can Bıyık, 2019. "Smart Cities in Turkey: Approaches, Advances and Applications with Greater Consideration for Future Urban Transport Development," Energies, MDPI, vol. 12(12), pages 1-33, June.
  5. Radfar, Shariat & Koosha, Hamidreza & Gholami, Ali & Amindoust, Atefeh, 2025. "A neuro-fuzzy and deep learning framework for accurate public transport demand forecasting: Leveraging spatial and temporal factors," Journal of Transport Geography, Elsevier, vol. 126(C).
  6. Gao, Fan & Han, Chunyang & Yang, Linchuan & Liang, Jian & He, Xuan & Li, Fan, 2024. "Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service," Journal of Transport Geography, Elsevier, vol. 115(C).
  7. Yang Zhang & Yongping Zhang & Jiangping Zhou, 2021. "A novel excess commuting framework: Considering commuting efficiency and equity simultaneously," Environment and Planning B, , vol. 48(1), pages 151-168, January.
  8. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
  9. Ma, Xinwei & Xie, Ruiyuan & Meng, Yu & Guo, Longxiao & Li, Zibiao, 2025. "Effects of catchment measurement on the associations between determinants and metro-ridesourcing integration," Journal of Transport Geography, Elsevier, vol. 128(C).
  10. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
  11. Tao, Sui & Rowe, Francisco & Shan, Hongyu, 2025. "Nonlinearities and threshold points in the effect of contextual features on the spatial and temporal variability of bus use in Beijing using explainable machine learning: Predictable or uncertain trips?," Journal of Transport Geography, Elsevier, vol. 123(C).
  12. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
  13. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
  14. Pezoa, Raúl & Basso, Franco & Quilodrán, Paulina & Varas, Mauricio, 2023. "Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile," Journal of Transport Geography, Elsevier, vol. 109(C).
  15. Giancarlos Parady & Kay W. Axhausen, 2024. "Size matters: the use and misuse of statistical significance in discrete choice models in the transportation academic literature," Transportation, Springer, vol. 51(6), pages 2393-2425, December.
  16. P. V. Stroev & S. B. Reshetnikov, 2017. "«Smart city» as a new stage of urban development," Russian Journal of Industrial Economics, MISIS, vol. 10(3).
  17. Yin, Ming & Fan, Yuqi & Wang, Yu, 2025. "Can TOD help metro station ridership ‘early recovery’ from COVID-19? An empirical evidence from Nanjing," Journal of Transport Geography, Elsevier, vol. 123(C).
  18. Jinjun Tang & Xiaolu Wang & Fang Zong & Zheng Hu, 2020. "Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
  19. Bi, Hui & Ye, Zhirui & Hu, Liyang & Zhu, He, 2021. "Why they don't choose bus service? Understanding special online car-hailing behavior near bus stops," Transport Policy, Elsevier, vol. 114(C), pages 280-297.
  20. Rong, Wuyue & Zhang, Zekun & Liu, Yang & Yang, Ying & Qu, Xiaobo, 2025. "Spatiotemporal heterogeneity exploration in the effects of TOD structural characteristics on metro ridership: Evidence from Shanghai," Journal of Transport Geography, Elsevier, vol. 128(C).
  21. Xia Li & Zhenyu Liu & Xinwei Ma, 2022. "Measuring Access and Egress Distance and Catchment Area of Multiple Feeding Modes for Metro Transferring Using Survey Data," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
  22. Zhang, Shanqi & Yang, Yu & Zhen, Feng & Lobsang, Tashi & Li, Zhixuan, 2021. "Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach," Journal of Transport Geography, Elsevier, vol. 90(C).
  23. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
  24. Lei, Da & Cheng, Long & Wang, Pengfei & Chen, Xuewu & Zhang, Lin, 2024. "Identifying service bottlenecks in public bikesharing flow networks," Journal of Transport Geography, Elsevier, vol. 116(C).
  25. Cefang Deng & An Jin, 2025. "A Demand-based Methodology for Urban Rail Transit Express Line Problem using Metaheuristic," Networks and Spatial Economics, Springer, vol. 25(4), pages 1097-1162, December.
  26. Zhang, Yuan & Chen, Xiao-Jian & Gao, Song & Gong, Yongxi & Liu, Yu, 2024. "Integrating smart card records and dockless bike-sharing data to understand the effect of the built environment on cycling as a feeder mode for metro trips," Journal of Transport Geography, Elsevier, vol. 121(C).
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