Visual Attention and Perceived Workload of E-Scooter Riders Across Selected Urban Route Conditions

Abstract

Understanding how selected urban route conditions are associated with e-scooter riders’ visual attention and perceived workload can support human-factor evaluation of micromobility environments. This study examines visual attention and perceived workload during real-world e-scooter riding across three selected urban routes in Zagreb with different infrastructure and traffic-environment characteristics. A field-based experimental methodology was used, integrating eye-tracking with post-ride perceived workload assessment through the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Twenty-eight adults, predominantly novice or occasional e-scooter riders, completed the three selected routes. Visual attention and perceived workload were examined across the selected routes; because only one route represented each route condition, the findings are interpreted as route-level evidence rather than general infrastructure-type effects. One-way repeated-measures analyses of variance (ANOVAs) showed that route condition had a significant effect on mean fixation duration and on the weighted NASA-TLX workload score, indicating that riders’ visual attention and perceived workload varied across the examined routes. To further examine these route-related differences, linear mixed-effects models were used as supporting analyses with participant ID as a random intercept. The results showed that R2 was associated with lower weighted workload than R1, while R3 was associated with higher weighted workload than R1. The route-complexity index was retained as an exploratory route-level descriptor rather than a validated continuous predictor. These findings highlight the importance of considering combined route, infrastructure, and traffic-environment characteristics when planning safer micromobility environments and developing appropriate regulations for e-scooter use. They also provide preliminary route-level human-factor evidence that can support future micromobility route evaluation and the development of human-centred guidance for infrastructure planning and e-scooter regulation.