Micromobility in Smart Cities: A Closer Look at Shared Dockless E-Scooters via Big Social Data

• URL: http://arxiv.org/abs/2010.15203v1
• Date: Wed, 28 Oct 2020 19:59:45 GMT
• Title: Micromobility in Smart Cities: A Closer Look at Shared Dockless E-Scooters via Big Social Data
• Authors: Yunhe Feng, Dong Zhong, Peng Sun, Weijian Zheng, Qinglei Cao, Xi Luo, Zheng Lu
• Abstract summary: Dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities. E-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues. This paper is the first large-scale systematic study on shared e-scooters using big social data.
• Score: 6.001713653976455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The micromobility is shaping first- and last-mile travels in urban areas. Recently, shared dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities due to the affordability, easy accessibility via an app, and zero emissions. Meanwhile, e-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues. In this paper, we collected and investigated 5.8 million scooter-tagged tweets and 144,197 images, generated by 2.7 million users from October 2018 to March 2020, to take a closer look at shared e-scooters via crowdsourcing data analytics. We profiled e-scooter usages from spatial-temporal perspectives, explored different business roles (i.e., riders, gig workers, and ridesharing companies), examined operation patterns (e.g., injury types, and parking behaviors), and conducted sentiment analysis. To our best knowledge, this paper is the first large-scale systematic study on shared e-scooters using big social data.

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