Suggested Citation:
Jena, Aurojeet, Basar Ozbilen, Alimurtaza Kothawala, Yongsung Lee, Kailai Wang, Charalampos Saridakis, Zia Wadud, Yuanxuan Yang, Susan Grant-Muller, Sebastian Castellanos, Giovanni Circella (2025)

Identifying E-Scooter Rider Profiles in the United States: A Latent Class Cluster Analysis

The global rise of e-scooters as a novel form of micromobility has reshaped urban transportation. Positioned as a sustainable mobility solution, transportation scholars underline that e-scooters have the potential to replace short car trips, reduce traffic congestion and carbon emissions, and increase energy efficiency of transportation systems. While existing literature has explored e-scooter adoption and trip characteristics extensively, there remains a research gap regarding the classification of e-scooter users. This paper attempts to overcome this deficiency by employing a latent class cluster analysis to classify e-scooter users based on their trip frequency, motivation, and purpose. Data used for the analysis were collected from e-scooter users in Washington, DC and Portland, OR in the United States. The results reveal two distinct classes of e-scooter users: ‘Joyriders’ and ‘Busyriders’. Joyriders tend to be young, female and belong to higher-income and vehicle-affluent households. They use e-scooters for leisure or tourism purposes, valuing pleasure and time savings. Busyriders are predominantly males aged 18–54 from vehicle-deficient or lower-income households and use e-scooters weekly for commuting, errands, and shopping purposes, prioritizing utilitarian and environmental benefits. The findings of this study can inform policymakers in developing effective e-scooter promotion policies, tailored to the preferences and needs of different user groups. The policy implications from this study can contribute towards the incorporation of e-scooters as a sustainable and efficient travel alternative into urban transportation.