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Parking behaviour analysis of shared E-bike users based on a real-world dataset - a case study in Dublin, Ireland

Yan, Sen, Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2022) Parking behaviour analysis of shared E-bike users based on a real-world dataset - a case study in Dublin, Ireland. In: IEEE 95th Vehicular Technology Conference: VTC2022-Spring, 19-22 June 2022, Helsinki, Finland.

Abstract
In recent years, an increasing number of shared E-bikes have been rolling out rapidly in our cities. It therefore becomes important to understand new behaviour patterns of the cyclists in using these E-bikes as a foundation for the novel design of shared micromobility services as part of the realisation for next generation intelligent transportation systems. In this paper, we deeply investigate the users' behaviour of shared E-bikes in a case study by using the real-world dataset collected from the shared E-bike company, MOBY, which currently operates in Dublin, Ireland. More specifically, we look into the parking behaviours of users as we know that inappropriate parking of these bikes can not only increase the management costs of the company but also result in other users' inconveniences, especially in situations of battery shortage, which inevitably reduces the overall operational efficacy of these shared E-bikes. Our work has conducted analysis at both bike station and individual level in a fully anonymous and GDPR-Compliant manner, and our results have shown that up to 12.9% of shared E-bike users did not park their bikes properly at the designated stands. Different visualisation tools have been applied to better illustrate our obtained results.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Shared E-Bikes; Parking Behaviour Analysis; Micromobility
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring). . IEEE.
Publisher:IEEE
Official URL:https://doi.org/10.1109/VTC2022-Spring54318.2022.9...
Copyright Information:© 2021 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland SFI Grant SFI/12/RC/2289 P2, European Regional Development Fund
ID Code:26782
Deposited On:21 Mar 2022 11:37 by Mingming Liu . Last Modified 15 Sep 2022 14:00
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