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Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland

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journal contribution
posted on 04.05.2021, 15:39 by Daniel J Reck, Haitao He, Sergio Guidon, Kay W Axhausen
Shared micromobility services (e-scooters, bikes, e-bikes) have rapidly gained popularity in the past few years, yet little is known about their usage. While most previous studies have analysed single modes, only few comparative studies of two modes exist and none so-far have analysed competition or mode choice at a high spatiotemporal resolution for more than two modes. To this end, we develop a generally applicable methodology to model and analyse shared micromobility competition and mode choice using widely accessible vehicle location data. We apply this methodology to estimate the first comprehensive mode choice models between four different micromobility modes using the largest and densest empirical shared micromobility dataset to-date. Our results suggest that mode choice is nested (dockless and docked) and dominated by distance and time of day. Docked modes are preferred for commuting. Hence, docking infrastructure for currently dockless modes could be vital for bolstering micromobility as an attractive alternative to private cars to tackle urban congestion during rush hours. Furthermore, our results reveal a fundamental relationship between fleet density and usage. A “plateau effect” is observed with decreasing marginal utility gains for increasing fleet densities. City authorities and service providers can leverage this quantitative relationship to develop evidence-based micromobility regulation and optimise their fleet deployment, respectively.



  • Architecture, Building and Civil Engineering

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Transportation Research Part C: Emerging Technologies




Elsevier BV


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© The Authors

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This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

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Dr Haitao He. Deposit date: 4 May 2021

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