A Two-step Microscopic Traffic Safety Evaluation Model of Reserved Lanes Facilities: An Arterial Case Study
Bibliographic record
Abstract
This study investigates the safety and operations of reserved lane facilities, with multiple interactions between vehicles. Currently, there are few studies that attempt to evaluate the impact on operations along the reserved lane with respect to the various geometric designs that allow for frequent locations of merging/diverging and crossing maneuvers. In this study, a VISSIM simulation model has been developed for a particular bus-lane arterial in Montreal area. The model was used to evaluate the safety and traffic operations along the corridor considering the status quo and some alternative design and prevailing traffic conditions scenarios, including different length of weaving sections and a modified geometric design of the entire bus-lane section. The simulation model was calibrated with real-world vehicle headways and its vehicle trajectories output were used in the Surrogate Safety Assessment Model (SSAM). In addition, an improvement of the SSAM model was achieved via a binary matrix calibration methodology in order to identify the vehicle conflicts more accurately. This improvement helps traffic analysts in properly identifying the potential traffic operations measures that contribute to a reduction of vehicular conflicts along the urban arterial. A comparison analysis has been conducted using a nearly one-kilometer bus-lane segment along the investigated arterial. The results show that traffic operations along the arterial, including the bus-lane, can be significantly improved if a different geometric alignment is deployed to reduce the number of interaction opportunities. For example, it is shown that establishing a 30-meter weaving section can significantly improve the vehicle's travel time as well as improve the safety performance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".