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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".