E-Scooter Dynamics: Unveiling Rider Behaviours and Interactions with Road Users through Multi-Modal Data Analysis.

Electric scooters (e-scooters), characterised by their small size and lightweight design, have revolutionised urban commuting experiences. Their adaptability to multiple mobility infrastructures introduces advantages for users, enhancing the efficiency and flexibility of urban transit. However, this versatility also causes potential challenges, including increased interactions and conflicts with other road users. Previous research has primarily focused on historical trip data, leaving a gap in our understanding of real-time e-scooter user behaviours and interactions. To bridge this gap, we propose a novel multi-modal data collection and integrated data analysis methodology, aimed at capturing the dynamic behaviours of e-scooter riders and their interactions with other road users in real-world settings. We present the study setup and the analysis approach we used for an in the wild study with 15 participants, each traversing a pre-determined route equipped with off-the-shelf commercially available devices (e.g., cameras, bike computers) and eye-tracking glasses.