Influence of mobile phone use on pedestrians at road crossings: insight from gait experiments
Bibliographic record
Abstract
Walking is fundamental to human beings, essential for survival, and a defining characteristic that sets us apart from other animals. This study investigates the impact of mobile phone distractions on pedestrian gait by conducting gait experiments. The main focus of this paper is to analyze three typical mobile phone usage modes: voice calls, texting, and listening to music (LtM). For the first two usage modes, two levels of secondary task difficulty (simple and complex) are further distinguished. For LtM, two types of music rhythm and style (slow-paced light music and fast-paced rock music) are considered. Differential analysis methods are used to analyze the experimental results. The results show that the impact of mobile phone distractions on pedestrian gait depends on the specific phone usage mode. Texting has the most significant impact, followed by voice calls and LtM. During walking with a voice call, pedestrians’ gait performance significantly decreases, and the difficulty level of the secondary task significantly affects gait characteristics and dual-task cost. Texting affects gait, direction, and distance perception, but task difficulty has little effect. Light music leads to slight reductions in walking speed and stride length. Also, only cognitive load is significantly influenced by the interaction of phone usage mode and task difficulty. This study provides insights into the influence of mobile phone distractions on pedestrian gait characteristics, highlighting the varying effects of different phone usage modes and secondary task difficulty levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".