Data-based insights into the usage of micromobility sharing
Abstract Free driving with electrically powered micromobility vehicles such as e-mopeds and e-scooters is an emerging mobility trend. This trend has also been visible in Germany since the ordinance on the use of electric microvehicles on public roads came into force in 2019. Car sharing and station-...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00251-8 |
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| _version_ | 1849767043453157376 |
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| author | I. Trautwein D. Ravlija M. Sonntag |
| author_facet | I. Trautwein D. Ravlija M. Sonntag |
| author_sort | I. Trautwein |
| collection | DOAJ |
| description | Abstract Free driving with electrically powered micromobility vehicles such as e-mopeds and e-scooters is an emerging mobility trend. This trend has also been visible in Germany since the ordinance on the use of electric microvehicles on public roads came into force in 2019. Car sharing and station-based bike sharing have been scientifically studied more often than the usage patterns and behavior of e-moped and e-scooter customers. Insights into usage patterns and customer behavior can be used to improve customer satisfaction and the business model, for example, to increase the utilization rate or distribution of e-mopeds or to offer customers more targeted incentives to perform operational activities. Similar to the existing scientific work, the data set of an e-moped supplier in Stuttgart, Germany is analyzed. Data were analyzed according to the (CRISP-DM) and a (RFM) analysis was performed. The data were clustered using different clustering configurations depending on the model used and the number of clusters. Clusters resulting from the highest performing configuration according to Calinski–Harabasz index (k-Means with 4 clusters) were further analyzed. The resulting clustering allows conclusions to be drawn about how customer usage patterns and behavior have changed compared to previous analyses in the literature. Examples of further findings are that one in five customers abandoned the registration process, or that early adopters were between 40 and 55 years old. |
| format | Article |
| id | doaj-art-16c18d9745b24e6e8fb037e5a5815841 |
| institution | DOAJ |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-16c18d9745b24e6e8fb037e5a58158412025-08-20T03:04:22ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-08-0112112210.1186/s43067-025-00251-8Data-based insights into the usage of micromobility sharingI. Trautwein0D. Ravlija1M. Sonntag2Fraunhofer Institute for Industrial EngineeringEsslingen University of Applied SciencesFraunhofer Institute for Industrial EngineeringAbstract Free driving with electrically powered micromobility vehicles such as e-mopeds and e-scooters is an emerging mobility trend. This trend has also been visible in Germany since the ordinance on the use of electric microvehicles on public roads came into force in 2019. Car sharing and station-based bike sharing have been scientifically studied more often than the usage patterns and behavior of e-moped and e-scooter customers. Insights into usage patterns and customer behavior can be used to improve customer satisfaction and the business model, for example, to increase the utilization rate or distribution of e-mopeds or to offer customers more targeted incentives to perform operational activities. Similar to the existing scientific work, the data set of an e-moped supplier in Stuttgart, Germany is analyzed. Data were analyzed according to the (CRISP-DM) and a (RFM) analysis was performed. The data were clustered using different clustering configurations depending on the model used and the number of clusters. Clusters resulting from the highest performing configuration according to Calinski–Harabasz index (k-Means with 4 clusters) were further analyzed. The resulting clustering allows conclusions to be drawn about how customer usage patterns and behavior have changed compared to previous analyses in the literature. Examples of further findings are that one in five customers abandoned the registration process, or that early adopters were between 40 and 55 years old.https://doi.org/10.1186/s43067-025-00251-8IncentiveClusteringCustomer segmentse-Moped sharing |
| spellingShingle | I. Trautwein D. Ravlija M. Sonntag Data-based insights into the usage of micromobility sharing Journal of Electrical Systems and Information Technology Incentive Clustering Customer segments e-Moped sharing |
| title | Data-based insights into the usage of micromobility sharing |
| title_full | Data-based insights into the usage of micromobility sharing |
| title_fullStr | Data-based insights into the usage of micromobility sharing |
| title_full_unstemmed | Data-based insights into the usage of micromobility sharing |
| title_short | Data-based insights into the usage of micromobility sharing |
| title_sort | data based insights into the usage of micromobility sharing |
| topic | Incentive Clustering Customer segments e-Moped sharing |
| url | https://doi.org/10.1186/s43067-025-00251-8 |
| work_keys_str_mv | AT itrautwein databasedinsightsintotheusageofmicromobilitysharing AT dravlija databasedinsightsintotheusageofmicromobilitysharing AT msonntag databasedinsightsintotheusageofmicromobilitysharing |