Distracted pedestrians crossing behaviour: Application of immersive head mounted virtual reality
Bibliographic record
Abstract
The use of virtual reality in transportation studies has gained interest in the past several years. Its ability to simulate real events in order to study perception and behaviour has led to safer and more controlled environments. In our study, an Immersive Head Mounted Virtual Reality (IHMVR) device is used to evaluate pedestrian crossing behaviour when 1) pedestrians are not distracted, 2) pedestrians are distracted with a hand held device, and 3) a safety measure is implemented on the road for distracted pedestrians with a hand held device. The proposed safety measure aims to alert distracted pedestrian by flashing LED lights on the crosswalk when pedestrian initiated crossing. A group of 25 students from Montréal, Canada, participated in the three crossing scenarios and their wait time, crossing time, speed, and acceleration were collected. For the safety analysis, both Time-to-Collision (TTC) and Post-Encroachment-Time (PET) surrogate measures were computed. The design and development of the road crossing implemented in the IHMVR is based on an existing road crossing in Montréal and its real time traffic information. The results from our study indicated safer crossing decisions from non-distracted pedestrian, compared to distracted pedestrian. The scenario with the implemented preventative measure did not improve safety, however it increased the successful crossing rate. Wait time for non-distracted pedestrian was shorter compared to distracted pedestrians who took longer to identify a safe crossing gap and initiate crossing. The crossing speed for distracted pedestrians with no safety countermeasure was higher due to their poor crossing choice compared to non-distracted participants.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".