Explaining shared micromobility usage, competition and mode choice by modelling empirical data from Zurich, Switzerland. (March 2021)

• Record Type: Journal Article
• Authors: Reck, Daniel J.; Haitao, He; Guidon, Sergio; Axhausen, Kay W.
• Is Part Of: Transportation research. Volume 124(2021)
• Publication Date: 2021-03
• Subjects: Micromobility -- E-scooter -- E-bike -- Bikesharing -- Competition -- Mode choice; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102947
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: First comprehensive choice models for 4 different shared micromobility modes. Generally applicable methodology for analysis at a high spatiotemporal resolution. Largest empirical dataset to-date. Nested mode choice (dockless, docked) with docked modes preferred for commuting. Fundamental relationship between fleet density and usage revealed. Abstract: 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.