Vehicular Safety and Operations Assessment of Reserved Lanes using Microscopic Simulation
Bibliographic record
Abstract
Evaluation of roadway safety via the analysis of vehicular conflicts using microscopic simulation shows increasing preference among transportation professionals, mostly due to significant advances in computational technology that allows for better efficiency when compared with other traffic safety modeling approaches. In addition, since modeling vehicular interactions via simulation is intrinsic to the methodology, one may assess various impacts of safety treatments without disrupting vehicle movements and before proceeding with real-world implementations. VISSIM, a microscopic traffic simulation model, is used in this thesis to reproduce vehicular interactions of an urban High Occupancy Vehicle (HOV) arterial in Québec. The model is calibrated to reflect the observed real-world driving behavior. Vehicle conflicts are assessed using the Surrogate Safety Assessment Model (SSAM) developed by Federal Highway Administration (FHWA). The experimental results indicate that the existing study area has a significant safety problem, mostly due to high interactions between buses and passenger cars. Alternative geometric and control designs are evaluated to ameliorate traffic safety. It is shown that the proposed alternative solutions can be used to either efficiently eliminate many vehicular traffic conflicts, or to significantly reduce public transit delay while ameliorating traffic safety. It is expected that this methodology can be successfully applied to other similar reserved lanes facilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".