Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers
Amit Kumar, Arman Hosseini, Arghavan Azarbayjani, Arsalan Heydarian, Omidreza Shoghli
TL;DR
This study investigates factors shaping the adoption of AI-assisted e-scooters by surveying 405 US participants and applying a decision-tree classifier to uncover sociodemographic patterns, alongside Structural Equation Modeling to test how safety perceptions and trust influence willingness. Key findings show that perceived safety in AI-enabled technologies and trust in AI-enabled e-scooters are the strongest predictors of willingness, while frequency of regular micromobility use and crash history have weaker or non-significant direct effects. Demographic factors such as ethnicity and education influence preferred AI adoption in nuanced ways, suggesting targeted outreach and literacy efforts could promote AI-enabled micromobility. The results highlight safety and trust as foundational to adoption, with practical implications for design, policy, and urban mobility planning.
Abstract
E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and perceived safety of both users and non-users. Artificial intelligence (AI), as a cutting-edge and widely applied technology, has demonstrated potential to enhance transportation safety, particularly in driver assistance systems. The integration of AI into e-scooters presents a promising approach to addressing these safety concerns. This study aims to explore the factors influencing individuals willingness to use AI-assisted e-scooters. Data were collected using a structured questionnaire, capturing responses from 405 participants. The questionnaire gathered information on demographic characteristics, micromobility usage frequency, road users' perception of safety around e-scooters, perceptions of safety in AI-enabled technology, trust in AI-enabled e-scooters, and involvement in e-scooter crash incidents. To examine the impact of demographic factors on participants' preferences between AI-assisted and regular e-scooters, decision tree analysis is employed, indicating that ethnicity, income, and age significantly influence preferences. To analyze the impact of other factors on the willingness to use AI-enabled e-scooters, a full-scale Structural Equation Model (SEM) is applied, revealing that the perception of safety in AI enabled technology and the level of trust in AI-enabled e-scooters are the strongest predictors.