Human Factors Assessment of Distraction, Workload, and Traffic Safety In Relation to Bus Operator Workload During Manual Stop Announcements
Bibliographic record
Abstract
In 2007 the Human Rights Tribunal of Ontario decided that all transit operators should manually call out stops in order to address the problem of accessibility for persons with visual impairments. In response to this requirement a study was carried out to determine the effect on traffic safety of having bus operators make manual stop announcements. Specifically the study was designed to determine whether the manual announcement of stops increased bus operators’ distraction levels to a significant degree, with a resulting adverse impact on operator health and safety and general public safety. An on-road experiment was carried out to compare the current situation in which no stops are announced with two conditions: the manual announcement of only major stops and the manual announcement of all stops. Data were collected on two busy routes with twelve on-duty operators, including six novice and six experienced transit operators. Two miniature cameras and a microphone were installed on the bus; one camera was aimed towards the bus operator and the other was oriented toward the road scene ahead. Five measures were selected to assess safety in relation to making manual bus stop announcements: stop announcement accuracy, glances to the list of stop names, glances to mirrors, subjective ratings of operator workload, and subjective ratings of perceived safety. The number and length of off-forward view glances were used to estimate changes in crash risk. On average, bus operators were able to announce all stops with an accuracy of 79.3%. With respect to the announcement of all stops as compared to no stops, bus operators spent time glancing at the stop name sheet while the bus was in motion (1.24 glances per minute). Some of these glances lasted over two seconds in duration (15.3 long glances per hour). While announcing all stops, bus operators spent less time looking at their mirrors when departing from a bus stop (4.01 vs. 5.06 seconds). Bus operators reported a higher workload when announcing all stops (18.1 vs. 8.8 on a 21-point scale) as compared to announcing no stops. Despite receiving training prior to their test session, and being specifically instructed not to look at the stop announcement sheet while in motion, all operators made glances to the stop name sheet while the bus was in motion. Based on experimental data showing that a two second glance away from the road doubles crash risk, if all operators on all routes were required to announce all stops, in the worst case there could be an increase of 2.8 to 5.6 crashes per year.
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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 | 0.006 |
| 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".