A spatiotemporal study and location-specific trip pattern categorization of shared e-scooter usage

authored by

Maximilian Heumann, Tobias Kraschewski, Tim Brauner, Lukas Tilch, Michael H. Breitner

Abstract

This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe’s most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.

Organisation(s)

Institute of Computer Science for Business Administration
Institute of Multiphase Processes

Type

Article

Journal

Sustainability (Switzerland)

Volume

13

ISSN

2071-1050

Publication date

12.11.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Geography, Planning and Development, Renewable Energy, Sustainability and the Environment, Environmental Science (miscellaneous), Energy Engineering and Power Technology, Management, Monitoring, Policy and Law

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy, SDG 11 - Sustainable Cities and Communities, SDG 15 - Life on Land

Electronic version(s)

https://doi.org/10.3390/su132212527 (Access: Open)