<?xml version="1.0"?>
<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Visual attention and perceived workload of e-scooter riders across selected urban route conditions</dc:title><dc:creator>Rizehbandi,	Shirin	(Avtor)
	</dc:creator><dc:creator>Fiolić,	Mario	(Avtor)
	</dc:creator><dc:creator>Babić,	Darko	(Avtor)
	</dc:creator><dc:creator>Cvahte Ojsteršek,	Tina	(Avtor)
	</dc:creator><dc:creator>Babić,	Dario	(Avtor)
	</dc:creator><dc:subject>e-scooter riding</dc:subject><dc:subject>eye-tracking</dc:subject><dc:subject>visual attention</dc:subject><dc:subject>perceived workload</dc:subject><dc:subject>sustainable micromobility</dc:subject><dc:description>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.</dc:description><dc:date>2026</dc:date><dc:date>2026-09-03 15:11:41</dc:date><dc:type>Članek v reviji</dc:type><dc:identifier>100064</dc:identifier><dc:identifier>UDK: 656.1</dc:identifier><dc:identifier>COBISS_ID: 289955843</dc:identifier><dc:identifier>DOI: 10.3390/su18178851</dc:identifier><dc:identifier>ISSN pri članku: 2071-1050</dc:identifier><dc:language>sl</dc:language></metadata>
