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 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...

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Autores principales:	Maximilian Heumann, Tobias Kraschewski, Tim Brauner, Lukas Tilch, Michael H. Breitner
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	e-scooter micro-mobility shared-mobility land use analysis spatiotemporal analysis spatial allocation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/4407646751e44e6db7e64635df2eade1
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id	oai:doaj.org-article:4407646751e44e6db7e64635df2eade1
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spelling	oai:doaj.org-article:4407646751e44e6db7e64635df2eade12021-11-25T19:01:47ZA Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage10.3390/su1322125272071-1050https://doaj.org/article/4407646751e44e6db7e64635df2eade12021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12527https://doaj.org/toc/2071-1050This 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.Maximilian HeumannTobias KraschewskiTim BraunerLukas TilchMichael H. BreitnerMDPI AGarticlee-scootermicro-mobilityshared-mobilityland use analysisspatiotemporal analysisspatial allocationEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12527, p 12527 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-scooter micro-mobility shared-mobility land use analysis spatiotemporal analysis spatial allocation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	e-scooter micro-mobility shared-mobility land use analysis spatiotemporal analysis spatial allocation Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Maximilian Heumann Tobias Kraschewski Tim Brauner Lukas Tilch Michael H. Breitner A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
description	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.
format	article
author	Maximilian Heumann Tobias Kraschewski Tim Brauner Lukas Tilch Michael H. Breitner
author_facet	Maximilian Heumann Tobias Kraschewski Tim Brauner Lukas Tilch Michael H. Breitner
author_sort	Maximilian Heumann
title	A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
title_short	A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
title_full	A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
title_fullStr	A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
title_full_unstemmed	A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage
title_sort	spatiotemporal study and location-specific trip pattern categorization of shared e-scooter usage
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4407646751e44e6db7e64635df2eade1
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A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage

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