Eye Movement Evaluation of Pedestrians' Mobile Phone Usage at Street Crossings
Bibliographic record
Abstract
Vision is one of the most important human senses, accounting for most of the external information pedestrians receive while crossing the street. However, distracted mobile phone usage during street crossing consumes pedestrians’ cognitive resources and diverts their visual attention. As a result, pedestrians may be unable to fully concentrate on observing the traffic environment and effectively planning their crossing path and behavior. This study evaluated the effect of pedestrian behavioral activities at street crossings on eye-movement (EM) characteristics. The crossing tasks were natural behavior, voice call, text messaging, and listening to music. The tasks were further categorized as simple or complex. A total of 29 participants were recruited in Nanjing: 18 males (62.1%) and 11 females (37.9%) with an average age of 23.59 years (SD = 2.44). The Friedman test was used to analyze differences in saccade frequency, fixation time, browsing number, and browsing time across different scenarios. Text messaging had the most significant impact on pedestrians’ EM characteristics, followed by voice call; music listening had a relatively weaker effect. Secondary task difficulty influenced the percentage of browsing, viewing, and to some extent gaze time. On the other hand, music rhythm and style only partially influenced the percentage of gaze and gaze time. Mobile phones substantially affected pedestrians’ EM characteristics and attention allocation for the same level of secondary task difficulty. These findings contribute to a better understanding of pedestrians’ visual characteristics under distracted mobile phone usage conditions and provide valuable insights for developing appropriate measures to enhance pedestrian safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".