An evaluation of the operations and safety issues at reserved lane facilities using microscopic video data and alternative methods
Bibliographic record
Abstract
This research investigates the operating performance and safety issues of reserved lanes in Quebec, Canada using microscopic data and different methods. Reserved lanes have been around for a few decades in North America, however their effectiveness and safety along congested facilities remains contested. This thesis proposes original methods for collecting microscopic data, analysing and evaluating the performance and safety of reserved lane highway and arterial facilities. The microscopic data (vehicle level data) is collected using video cameras and obtained with an open-source feature-based tracking software. As a first objective, this research work evaluates the accuracy of the automatically calculated speed data by comparing them to manually collected speeds from different camera orientations and concludes that the mean errors are comparable to the current collection technologies. The second part of the research applies the tracked vehicle data from an exclusive bus lane to a microsimulation scenario and calibrates the speeds along the corridor to estimate its effects on the overall performance and emissions. The third section analyses the performance and safety along highway and arterial facilities. The highway segment was evaluated before and after the implementation of an HOV lane by investigating the differences in volumes, speeds and travel times. The arterial segment with a reserved bus lane was evaluated based on lane change violation rates and surrogate safety indicators calculated using trajectory-based tools. Countermeasures for both facilities are discussed based on the results extracted using the alternative data collection process. Overall, the research highlights the video-based application's ability to generate microscopic traffic data and presents new methods that can be implemented along facilities to monitor, build microscopic models and evaluate reserved lanes based on different driver behavior indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".