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Estimating E-Scooter Traffic Flow Using Big Data to Support Planning for Micromobility

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  • Chen Feng
  • Junfeng Jiao
  • Haofeng Wang

Abstract

Dockless e-scooter sharing, as a new shared micromobility service, has quickly gained popularity in recent years. In this paper, we present a practical approach to estimating e-scooter flow patterns without knowing the actual routes taken by the e-scooter riders. Our method takes advantage of a huge open dataset that contains the origins and destinations of millions of trips. We show that our models can help cities better support the emerging shared micromobility service. The additional information generated in the modeling process can also be useful for a more refined analysis of e-scooter trips.

Suggested Citation

  • Chen Feng & Junfeng Jiao & Haofeng Wang, 2022. "Estimating E-Scooter Traffic Flow Using Big Data to Support Planning for Micromobility," Journal of Urban Technology, Taylor & Francis Journals, vol. 29(2), pages 139-157, April.
    • Handle: RePEc:taf:cjutxx:v:29:y:2022:i:2:p:139-157
    DOI: 10.1080/10630732.2020.1843384
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    Cited by:

    1. Panagiotis G. Tzouras & Lambros Mitropoulos & Katerina Koliou & Eirini Stavropoulou & Christos Karolemeas & Eleni Antoniou & Antonis Karaloulis & Konstantinos Mitropoulos & Eleni I. Vlahogianni & Kons, 2023. "Describing Micro-Mobility First/Last-Mile Routing Behavior in Urban Road Networks through a Novel Modeling Approach," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    2. Yin, Zehui & Rybarczyk, Greg & Zheng, Anran & Su, Lin & Sun, Bingrong & Yan, Xiang, 2024. "Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset," Journal of Transport Geography, Elsevier, vol. 114(C).

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