Data-based Usage Analysis of Shared e-scooters in the Context of Public Transport

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Conference Proceedings
Authors: Rafael OehmeWaldemar TitovKonstantin KraussTill GnannThomas Schlegel

Abstract: Shared e-scooters could improve the sustainability of traffic by being an incentive to switch to public transportation and simultaneously being a potential first- and last-mile-solution. Currently, it is not clear whether e-scooter sharing is a positive addition or an additional burden for urban traffic. This work evaluates the usage of shared e-scooters and its impacts on public transport, based on movement data of e-scooters distributed in the German city of Karlsruhe. A central research question answered in this work is whether shared e-scooters contribute to increasing the attractiveness of public transport or whether they substitute it. Since this question has hardly been investigated so far with the help of mobility data, this work addresses a research gap.Therefore, a concept is presented that can be used to perform a usage analysis of shared e-scooters. In particular, the question will be addressed to what extent shared e-scooters are used for the first- or last-mile and how often they substitute public transport. Furthermore, it will be assessed how suitable our approach is to investigate the mentioned questions.To answer the research questions, we present methods with which the raw e-scooter data, collected via the publicly available APIs, is processed, and the investigation is carried out. In a first step, the shared e-scooter data is processed as plausible paths with the help of our customized python analysis tools. The intermediate result should be e-scooter paths that can be interpreted as trips made by users. Therefore, we applied a similar approach as in comparable studies [1-3].The data basis is validated by calculating key figures and performing a temporal-spatial usage analysis. Through a comparison with the results of similar studies [1-3] it can be assessed to what extent the data is representative and whether special features can be found in the study area. In addition, external influences such as weather conditions or covid-19-related restrictions that may have acted during the study period were considered.The e-scooter trips are then categorized to assess potential use in the context of public transport use. For this categorization, the shared e-scooter trips are divided based on their proximity to public transport stops into four categories: access, departure, substitution, and other trips. An access trip is defined as a potential first-mile trip, departure trips are potential last-mile trips and substitution trips are considered as potential public transport substitutions. More than a third of the studied shared e-scooter trips could have potentially been made using public transportation. In the full paper, the results of our study are interpreted, and the used approaches are evaluated. The focus is on estimating, how many of the access and departure trips are actual first and last mile trips. It could be estimated that, with a share of about 18%, shared e-scooters are used for the first- and last-mile, about as often as surveys in France (15%) and Portland (12%) show [4]. Finally, supplemented by public transport passenger data, we investigate whether statistical correlations between shared e-scooter and public transport use can be proven.References:[1] McKenzie, Grant (2019): Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C. Journal of Transport Geography. https://www.researchgate.net/publication/333543134_Spatiotemporal_comparative_analysis_of_scooter-share_and_bike-share_usage_patterns_in_Washington_DC[2]Reck, Daniel; Haitao, He; Guidon, Sergio; Axhausena, Kay W. (2021): Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland. Volume 124. Transportation Research Part C: Emerging Technologies. https://www.sciencedirect.com/science/article/pii/S0968090X20308445, [3] Caspi, O.; Smart, M.; Noland, R. (2020): Spatial associations of dockless shared e-scooter usage. Volume 86. Transportation Research Part D: Transport and Environment. https://www.sciencedirect.com/science/article/abs/pii/S1361920920305836[4] Gubman, Joanna; Jung, Alexander; Kiel, Thomas; Strehmann, Jan (2019): Shared E-Scooters: Paving the Road Ahead, Policy Recommendations for Local Government. Berlin: Agora Verkehrswende.

Keywords: Shared E-Scooter, Last-Mile-Solution, Public Transport, Movement Data

DOI: 10.54941/ahfe100990

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