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Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data

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  • Zhou, Jiangping
  • Murphy, Enda
  • Long, Ying

Abstract

Using Beijing as an example, this research demonstrates that smartcard data can be used to (a) assemble the required data for excess commuting studies, and (b) visualise related results. Based on both smartcard and household travel survey data, we find that the theoretical minimum commute is considerably lower for bus users than for car users in Beijing. This suggests that there is a greater inter-mixing of jobs–housing functions (i.e., a better jobs–housing balance) associated with users of that mode compared to the corresponding land-use arrangement for car users, who locate further from the central area (Tian’anmen) than bus users. The commuting range for car users is 9.4km greater than for bus users. Excess commuting is slightly higher for bus users (69.5%) than for car users (68.8%). Commuting capacity values are slightly lower for car users than for bus users, implying that car users consume less of their available commuting resources overall than bus users, albeit only marginally.

Suggested Citation

  • Zhou, Jiangping & Murphy, Enda & Long, Ying, 2014. "Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data," Journal of Transport Geography, Elsevier, vol. 41(C), pages 175-183.
  • Handle: RePEc:eee:jotrge:v:41:y:2014:i:c:p:175-183
    DOI: 10.1016/j.jtrangeo.2014.09.006
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    Cited by:

    1. 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.
    2. Xiang Zhou & Xiaohong Chen & Tianran Zhang, 2016. "Impact of Megacity Jobs-Housing Spatial Mismatch on Commuting Behaviors: A Case Study on Central Districts of Shanghai, China," Sustainability, MDPI, vol. 8(2), pages 1-22, January.
    3. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    4. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    5. Cecilia Wong & Wei Zheng & Miao Qiao, 2020. "Urban expansion and neighbourhood commuting patterns in the Beijing metropolitan region: A multilevel analysis," Urban Studies, Urban Studies Journal Limited, vol. 57(13), pages 2773-2793, October.
    6. Qi-Li Gao & Qing-Quan Li & Yan Zhuang & Yang Yue & Zhen-Zhen Liu & Shui-Quan Li & Daniel Sui, 2019. "Urban commuting dynamics in response to public transit upgrades: A big data approach," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
    7. Chen, Ruoyu & Zhang, Min & Zhou, Jiangping, 2023. "Jobs-housing relationships before and amid COVID-19: An excess-commuting approach," Journal of Transport Geography, Elsevier, vol. 106(C).
    8. Yixiao Li & Zhaoxin Dai & Lining Zhu & Xiaoli Liu, 2019. "Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    9. Cheng, Lin & Chen, Chen & Xiu, Chunliang, 2017. "Excess kindergarten travel in Changchun, Northeast China: A measure of residence-kindergarten spatial mismatch," Journal of Transport Geography, Elsevier, vol. 60(C), pages 208-216.
    10. Saadi, Ismaïl & Boussauw, Kobe & Teller, Jacques & Cools, Mario, 2016. "Trends in regional jobs-housing proximity based on the minimum commute: The case of Belgium," Journal of Transport Geography, Elsevier, vol. 57(C), pages 171-183.
    11. Zhang, Hong & Xu, Shan & Liu, Xuan & Liu, Chengliang, 2021. "Near “real-time” estimation of excess commuting from open-source data: Evidence from China's megacities," Journal of Transport Geography, Elsevier, vol. 91(C).
    12. Bwire, Hannibal & Zengo, Emil, 2020. "Comparison of efficiency between public and private transport modes using excess commuting: An experience in Dar es Salaam," Journal of Transport Geography, Elsevier, vol. 82(C).
    13. Songkorn Siangsuebchart & Sarawut Ninsawat & Apichon Witayangkurn & Surachet Pravinvongvuth, 2021. "Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand," Sustainability, MDPI, vol. 13(4), pages 1-29, February.
    14. 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).
    15. Xiping Yang & Zhixiang Fang & Ling Yin & Junyi Li & Yang Zhou & Shiwei Lu, 2018. "Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    16. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    17. Yang, Binyu & Tian, Yuan & Wang, Jian & Hu, Xiaowei & An, Shi, 2022. "How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation," Transport Policy, Elsevier, vol. 127(C), pages 1-14.
    18. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    19. Bi, Hui & Li, Aoyong & Hua, Mingzhuang & Zhu, He & Ye, Zhirui, 2022. "Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai," Transport Policy, Elsevier, vol. 129(C), pages 51-65.
    20. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    21. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    22. Zhou, Jiangping & Murphy, Enda, 2019. "Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 74(C), pages 223-232.
    23. Haonan Zhang & Hu Zhao & Saisai Meng & Yanghua Zhang, 2022. "Research on the Jobs-Housing Balance of Residents in Peri-Urbanization Areas in China: A Case Study of Zoucheng County," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    24. Jiangping Zhou & Ying Long, 2016. "Losers and Pareto optimality in optimising commuting patterns," Urban Studies, Urban Studies Journal Limited, vol. 53(12), pages 2511-2529, September.
    25. Zhao, Pengjun & Zhang, Yixue, 2019. "The effects of metro fare increase on transport equity: New evidence from Beijing," Transport Policy, Elsevier, vol. 74(C), pages 73-83.

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