2008 Travel Time Study: Advances in System Performance Measurement
Bibliographic record
Abstract
The Ministry of Transportation (MTO) has undertaken a biennial travel time survey of the major provincial roadways in the Greater Toronto Area (GTA) since 1996. The results of the studies provide metrics for assessing facility performance and identifying critical roads sections to be considered in future improvement projects and traffic management strategies. These studies have evolved over time, transitioning from manual methods of travel time data collection to global position system (GPS)-based methods, and expanding the breadth of analyses to include performance measures, such as TTI, BTI and arterial delay. This report describes techniques used in the 2008 Travel Time Study that could improve the process and results of subsequent travel time studies. Such techniques include the automated, GPS-based data collection approach to determining signal delay on arterials. This method replaced manual processes, reducing the potential of error and reducing safety risks to surveyors. Another technique was the concurrent surveying of highways and parallel arterial corridor(s), which facilitated a direct comparison of speeds on the parallel routes in the same time frame. This comparison found that while parallel route data could not always be correlated, it did provides some insight into the time within the peak period when motorists may begin to seek alternative routes to their normal commutes. A comparison of passenger car to truck probe data was also conducted, revealing that while truck speeds are almost always slower than car speeds in high speed conditions, they may still be representative of car speeds during other operating conditions. The report outlines how the techniques used in the 2008 Travel Time Study could be refined and re-applied.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".