IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v97y2021ics0966692321002799.html
   My bibliography  Save this article

Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit

Author

Listed:
  • Zhou, Yirong
  • Liu, Xiaoyue Cathy
  • Grubesic, Tony

Abstract

Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.

Suggested Citation

  • Zhou, Yirong & Liu, Xiaoyue Cathy & Grubesic, Tony, 2021. "Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit," Journal of Transport Geography, Elsevier, vol. 97(C).
    • Handle: RePEc:eee:jotrge:v:97:y:2021:i:c:s0966692321002799
    DOI: 10.1016/j.jtrangeo.2021.103226
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692321002799
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2021.103226?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    2. Aybike Ongel & Erik Loewer & Felix Roemer & Ganesh Sethuraman & Fengqi Chang & Markus Lienkamp, 2019. "Economic Assessment of Autonomous Electric Microtransit Vehicles," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    3. Wei, Ran & Liu, Xiaoyue & Ou, Yi & Kiavash Fayyaz, S., 2018. "Optimizing the spatio-temporal deployment of battery electric bus system," Journal of Transport Geography, Elsevier, vol. 68(C), pages 160-168.
    4. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    5. Luyu Liu & Harvey J Miller & Jonathan Scheff, 2020. "The impacts of COVID-19 pandemic on public transit demand in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    6. Jiang, Yonglei & Timmermans, Harry J.P. & Yu, Bin, 2018. "Relocation of manufacturing industry from the perspective of transport accessibility – An application of percolation theory," Transport Policy, Elsevier, vol. 63(C), pages 10-29.
    7. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    8. Mark Horner & Tony Grubesic, 2001. "A GIS-based planning approach to locating urban rail terminals," Transportation, Springer, vol. 28(1), pages 55-77, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Rath, Srushti & Liu, Bingqing & Yoon, Gyugeun & Chow, Joseph Y.J., 2023. "Microtransit deployment portfolio management using simulation-based scenario data upscaling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar, 2022. "Spatiotemporal evolving patterns of bike-share mobility networks and their associations with land-use conditions before and after the COVID-19 outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    3. Rauf Ahmed Shams Malick & Syed Kashir Hasan & Fahad Samad & Nadeem Kafi Khan & Hassan Jamil Syed, 2023. "Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques," Sustainability, MDPI, vol. 15(6), pages 1-17, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    2. Dessouky, Maged M & Hu, Shichun, 2021. "Dynamic Routing for Ride-Sharing," Institute of Transportation Studies, Working Paper Series qt6qq8r7hz, Institute of Transportation Studies, UC Davis.
    3. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    4. Daganzo, Carlos F. & Ouyang, Yanfeng & Yang, Haolin, 2020. "Analysis of ride-sharing with service time and detour guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 130-150.
    5. Hosni, Hadi & Naoum-Sawaya, Joe & Artail, Hassan, 2014. "The shared-taxi problem: Formulation and solution methods," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 303-318.
    6. Bhoopalam, Anirudh Kishore & Agatz, Niels & Zuidwijk, Rob, 2018. "Planning of truck platoons: A literature review and directions for future research," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 212-228.
    7. Stiglic, M. & Agatz, N.A.H. & Savelsbergh, M.W.P. & Gradisar, M., 2016. "Enhancing Urban Mobility: Integrating Ride-sharing and Public Transit," ERIM Report Series Research in Management ERS-2016-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Tao Yang & Weixin Wang, 2022. "Logistics Network Distribution Optimization Based on Vehicle Sharing," Sustainability, MDPI, vol. 14(4), pages 1-12, February.
    9. Xing Wang & Niels Agatz & Alan Erera, 2018. "Stable Matching for Dynamic Ride-Sharing Systems," Transportation Science, INFORMS, vol. 52(4), pages 850-867, August.
    10. Wang, Jing-Peng & Ban, Xuegang (Jeff) & Huang, Hai-Jun, 2019. "Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 390-415.
    11. Masoud, Neda & Jayakrishnan, R., 2017. "A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 1-29.
    12. Sunghi An & Daisik Nam & R. Jayakrishnan & Soongbong Lee & Michael G. McNally, 2021. "A Study of the Factors Affecting Multimodal Ridesharing with Choice-Based Conjoint Analysis," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    13. Rajendran, Suchithra & Srinivas, Sharan & Grimshaw, Trenton, 2021. "Predicting demand for air taxi urban aviation services using machine learning algorithms," Journal of Air Transport Management, Elsevier, vol. 92(C).
    14. Peng, Zixuan & Shan, Wenxuan & Zhu, Xiaoning & Yu, Bin, 2022. "Many-to-one stable matching for taxi-sharing service with selfish players," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 255-279.
    15. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(C).
    16. Lei, Chao & Jiang, Zhoutong & Ouyang, Yanfeng, 2020. "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 60-75.
    17. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    18. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    19. Boysen, Nils & Briskorn, Dirk & Schwerdfeger, Stefan, 2019. "Matching supply and demand in a sharing economy: Classification, computational complexity, and application," European Journal of Operational Research, Elsevier, vol. 278(2), pages 578-595.
    20. Berrada, Jaâfar & Poulhès, Alexis, 2021. "Economic and socioeconomic assessment of replacing conventional public transit with demand responsive transit services in low-to-medium density areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 317-334.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:97:y:2021:i:c:s0966692321002799. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.