A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

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