IDEAS home Printed from https://ideas.repec.org/r/plo/pone00/0149222.html
   My bibliography  Save this item

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Peikun Li & Chaoqun Ma & Jing Ning & Yun Wang & Caihua Zhu, 2019. "Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
  2. Zhou, Jiangping & Sipe, Neil & Ma, Zhenliang & Mateo-Babiano, Derlie & Darchen, Sébastien, 2019. "Monitoring transit-served areas with smartcard data: A Brisbane case study," Journal of Transport Geography, Elsevier, vol. 76(C), pages 265-275.
  3. Florian Dandl & Michael Hyland & Klaus Bogenberger & Hani S. Mahmassani, 2019. "Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets," Transportation, Springer, vol. 46(6), pages 1975-1996, December.
  4. Lijie Yu & Quan Chen & Kuanmin Chen, 2019. "Deviation of Peak Hours for Urban Rail Transit Stations: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
  5. Zhong, Jiaming & He, Zhaocheng & Tian, Chenyu, 2019. "Uncovering quasi-periodicity of transit behavior based on smart card data," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
  6. Zhang, Xiaohu, 2021. "Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service," Journal of Transport Geography, Elsevier, vol. 95(C).
  7. Zi-jia Wang & Hai-xu Liu & Shi Qiu & Ji-ping Fang & Ting Wang, 2019. "The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology," Sustainability, MDPI, vol. 11(21), pages 1-16, November.
  8. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
  9. Varvara Nikulina & David Simon & Henrik Ny & Henrikke Baumann, 2019. "Context-Adapted Urban Planning for Rapid Transitioning of Personal Mobility towards Sustainability: A Systematic Literature Review," Sustainability, MDPI, vol. 11(4), pages 1-37, February.
  10. Nir Kaplan & Itzhak Omer, 2022. "Multiscale Accessibility—A New Perspective of Space Structuration," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
  11. Deng, Yue & Wang, Jiaxin & Gao, Chao & Li, Xianghua & Wang, Zhen & Li, Xuelong, 2021. "Assessing temporal–spatial characteristics of urban travel behaviors from multiday smart-card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
  12. Hongxia Feng & Yaotong Chen & Jinyi Wu & Zhenqian Zhao & Yuanqing Wang & Zhuoting Wang, 2023. "Urban Rail Transit Station Type Identification Based on “Passenger Flow—Land Use—Job-Housing”," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
  13. Jadrić Mario & Pašalić Ivana Ninčević & Ćukušić Maja, 2020. "Process Mining Contributions to Discrete-event Simulation Modelling," Business Systems Research, Sciendo, vol. 11(2), pages 51-72, October.
  14. Šveda, Martin & Madajová, Michala Sládeková, 2023. "Estimating distance decay of intra-urban trips using mobile phone data: The case of Bratislava, Slovakia," Journal of Transport Geography, Elsevier, vol. 107(C).
  15. 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.
  16. Mu Lin & Zhengdong Huang & Tianhong Zhao & Ying Zhang & Heyi Wei, 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data," Sustainability, MDPI, vol. 14(15), pages 1-16, August.
  17. Blume, Steffen O.P. & Corman, Francesco & Sansavini, Giovanni, 2022. "Bayesian origin-destination estimation in networked transit systems using nodal in- and outflow counts," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 60-94.
  18. Wen, Jian & Nassir, Neema & Zhao, Jinhua, 2019. "Value of demand information in autonomous mobility-on-demand systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 346-359.
  19. Åse Jevinger & Jan A. Persson, 2019. "Exploring the potential of using real-time traveler data in public transport disturbance management," Public Transport, Springer, vol. 11(2), pages 413-441, August.
  20. 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).
  21. Marina Toger & Ian Shuttleworth & John Osth, 2020. "How average is average? Temporal patterns in human behaviour as measured by mobile phone data -- or why chose Thursdays," Papers 2005.00137, arXiv.org.
  22. Ying Zhao & Jie Wei & Haijun Li & Yan Huang, 2024. "Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
  23. Jiaoe Wang & Yanan Li & Jingjuan Jiao & Haitao Jin & Fangye Du, 2023. "Bus ridership and its determinants in Beijing: A spatial econometric perspective," Transportation, Springer, vol. 50(2), pages 383-406, April.
  24. Zhang, Yuerong & Marshall, Stephen & Manley, Ed, 2021. "Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area," Journal of Transport Geography, Elsevier, vol. 94(C).
  25. Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
  26. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.