Speed, Travel Time, and Delay for Intersections and Road Segments in Montreal Using Cyclist Smartphone GPS Data
Bibliographic record
Abstract
Until now, very little has been known about cyclist speeds and delays at the disaggregate level of each road segment and intersection. Speeds and delays serve as vital information for navigation and routing purposes since they can identify speeds and delays during different times of the day and how they differ across roads and bicycle facilities. In this work, the authors explore the use of recent GPS cyclist trip data, from the Mon ResoVelo smartphone application, for identifying different level-of-service measures such as travel time, speed and delay at the level of the entire road and intersection network for the island of Montreal. Also, a linear regression model is formulated to identify the geometric design and built environment characteristics affecting cyclist speeds on segments. Among other results, on average, segment speeds are greater along arterials than on local streets and greater along segments with bicycle infrastructure than those without. Modeling cyclist speed revealed that the variable representing the cyclists’ average speed on uphill, downhill and level segments, cyclists’ average speed on arterials as well as geometric design, built environment affect segment speeds. The model results identify that segments which have cyclists biking for work or school related purposes, segments used during morning peak, segments with bicycle infrastructure and segments which do not have signalized intersections at either end, tend to have cyclists riding at greater speeds. Also, cyclists travel faster when the temperature is between 10°and 20° and travel slower late at night or early morning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".