Impact of Different Infrastructures and Traffic Scenarios on Behavioral and Physiological Responses of E-scooter Users
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Summary
This study addresses the growing safety concerns associated with electric scooter (e-scooter) use, motivated by a 127% increase in micromobility-related injuries between 2017 and 2021. While previous research relied on emergency records or observational surveys, this paper fills a gap by examining the nuanced situational interactions between riders and their environment through naturalistic data. The research aims to understand how road infrastructure and specific traffic scenarios influence e-scooterists’ riding behaviors, gaze patterns, and cognitive load to inform safer urban planning. The researchers conducted a naturalistic study using instrumented Ninebot MAX e-scooters equipped with Tobii Pro Glasses 3 for eye-tracking, a SpeedTracker app for GPS and speed data, and a smartwatch for heart rate monitoring. Five participants completed approximately 20 hours of riding across diverse routes in Charlottesville, Virginia, including sidewalks, pedestrian trails, bike lanes, and shared roads. The study analyzed gaze metrics—such as fixation duration, Stationary Gaze Entropy (SGE), and Gaze Transition Entropy (GTE)—alongside head movement variability across eight specific traffic scenarios, including intersections, passing buses, and downhill riding. Results indicate that infrastructure type significantly impacts rider behavior. Bike lanes provided a stable environment characterized by reduced horizontal head movement and focused attention on the road surface, whereas shared roads and sidewalks led to dispersed gaze patterns and increased head movement, signaling higher uncertainty. Traffic scenarios involving interactions with other road users demanded greater cognitive load. Intersections required heightened visual focus and spatial awareness, evidenced by increased horizontal eye and head movements. When riding in close proximity to cars or passing buses, riders prioritized visual scanning over head movement to maintain stability while monitoring hazards. High-speed downhill riding resulted in the most concentrated gaze patterns, reflecting intense focus on obstacles and the road surface. Elevated entropy values in complex scenarios like intersections and downhill paths confirmed increased cognitive processing demands. The findings demonstrate that e-scooter riders adapt their visual and physical responses to environmental complexity, with dedicated infrastructure reducing cognitive load and uncertainty. The study highlights that interactions with larger vehicles and complex intersections pose significant safety risks due to the high attention required. These insights provide a foundation for improving road infrastructure and safety policies. The authors conclude that future research should expand participant demographics and incorporate additional physiological metrics, such as EEG and galvanic skin response, to further understand the multidimensional challenges faced by e-scooter users.
Key finding
Bike lanes provide a more stable and predictable riding environment with reduced horizontal head movement and focused attention, while complex traffic scenarios like intersections and vehicle interactions demand higher cognitive load and dispersed visual scanning.
Methodology
naturalistic
Sample size: 5
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-27.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-27 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | skipped | — | — | — | 4 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data, observational prevalence
- Methodological Resource: tool software