It’s a Matter of Time: Assessment of Additional Time Budgeted for Commuting to McGill University Across Modes
Bibliographic record
Abstract
Commute travel time is not always reliable, and individuals often budget additional time to ensure that they arrive at their destination punctually. This additional time allotted for the commute needlessly reduces the amount of time that individuals could have spent performing other activities. This study investigates the amount of additional time commuters allocate to account for travel time unreliability and presents the results with a series of log-linear regression models. Data for this study originated from the 2013 McGill Commuter Survey, a universitywide survey in which students, staff, and faculty described their typical experience commuting to McGill University, located in Montreal, Quebec, Canada. Results reveal that drivers allocate the most extra time for their commute, whereas users of other modes (transit, bicycle, and pedestrian) budget about 29% to 66% less than drivers. The findings of this study also indicate that bus commuters add 14% more buffer time per bus taken, and train users budget 11% less time for every commuter train taken. These findings reveal an existing perception that the street network is unreliable (for either buses or cars). Hence, the city should consider implementing strategies such as exclusive bus lanes and variable cost congestion pricing schemes to reduce uncertainty in travel time and improve the reliability of the street network. Such strategies are expected to decrease the level of uncertainty related to commuting to work or school and accordingly reduce the amount of time lost because of additional time budgeted for uncertainty.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".