Impact of Smartphone Distraction on Pedestrians' Crossing Behaviour: An\n Application of Head-Mounted Immersive Virtual Reality
Bibliographic record
Abstract
A novel head-mounted virtual immersive/interactive reality environment (VIRE)\nis utilized to evaluate the behaviour of participants in three pedestrian road\ncrossing conditions while 1) not distracted, 2) distracted with a smartphone,\nand 3) distracted with a smartphone with a virtually implemented safety measure\non the road. Forty-two volunteers participated in our research who completed\nthirty successful (complete crossing) trials in blocks of ten trials for each\ncrossing condition. For the two distracted conditions, pedestrians are engaged\nin a maze-solving game on a virtual smartphone, while at the same time checking\nthe traffic for a safe crossing gap. For the proposed safety measure, smart\nflashing and color changing LED lights are simulated on the crosswalk to warn\nthe distracted pedestrian who initiates crossing. Surrogate safety measures as\nwell as speed information and distraction attributes such as direction and\norientation of participant's head were collected and evaluated by employing a\nMultinomial Logit (MNL) model. Results from the model indicate that females\nhave more dangerous crossing behaviour especially in distracted conditions;\nhowever, the smart LED treatment reduces this negative impact. Moreover, the\nnumber of times and the percentage of duration the head was facing the\nsmartphone during a trial and a waiting time respectively increase the\npossibility of unsafe crossings; though, the proposed treatment reduces the\nsafety crossing rate. Hence, our study shows that the smart LED light safety\ntreatment indeed improves the safety of distracted pedestrians and enhances the\nsuccessful crossing rate.\n
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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 | 0.000 | 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".