ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume VIII-4/W1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W1-2021, 41–47, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-41-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W1-2021, 41–47, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-41-2021

  03 Sep 2021

03 Sep 2021

DATA-BASED APPLICATION SCENARIOS FOR E-SCOOTERS

J. Fauser, N. Sigle, and D. Hertweck J. Fauser et al.
  • Faculty of Computer Science and Reutlingen Research Institute, Reutlingen University, Germany

Keywords: E-sccoter sharing, Data Mining, Clustering, E-Mobility, Free-floating

Abstract. In various German cities free-floating e-scooter sharing is an upcoming trend in e-mobility. Trends such as climate change, urbanization, demographic change, amongst others are arising and forces the society to develop new mobility solutions. Contrasting the more scientifically explored car sharing, the usage patterns and behaviors of e-scooter sharing customers still need to be analyzed. This presumably enables a better addressing of customers as well as adaptions of the business model to increase scooter utilization and therefore the profit of the e-scooter providers. The customer journey is digitally traceable from registration to scooter reservation and the ride itself. These data enable to identifies customer needs and motivations. We analyzed a dataset from 2017 to 2019 of an e-scooter sharing provider operating in a big German city. Based on the datasets we propose a customer clustering that identifies three different customer segments, enabling to draw multiple conclusions for the business development and improving the problem-solution fit of the e-scooter sharing model.