Improving safety and traffic operations in urban zones with a high propensity for rear-end collisions at signalized intersections
Bibliographic record
Abstract
At signalized intersections, rear-end accidents are frequently the predominant accident type. These accidents result from the combination of a lead-vehicle's decelereation and the ineffective response of the following vehicle's driver to this deceleration. The frequency and severity of rear end collisions can be reduced through traffic control and operational improvements. There are different traffic operation strategies for rear-end collision reduction like "Employ multiphase signal operation". "optimize clearance intervals" or "Restrict or eliminate turning maneuvers (including right turns on red)". In practice, the most cost-effective strategy appears to be "Employ Signal Coordination". The first objective of this research report was to use spatial analysis tools to disaggregate Toronto into 76 spatial zones (Toronto is usually divided into 158 zones). The second objective was to rank the 76 zones according to their propensity for rear-end collisions at 4-legged signalized intersections. The third objective was to demonstrate how safety can be improved through signal coordination and progression. The software package Synchro-4.00 was used to recommend improved signal coordination through optimization of cycle lenghts, splits and offsets. The coordination analysis for the 15 intersectins located in the zone with the highest propensity for rear-end collision revealed the following results. 1. Coordination was definitely not recommended for three of the intersections. 2. Coordination was probably not recommended for six of the intersections; and 3. Coordination was definitely recommended for six of the intersections. The coordinatability analysis for the 15 intersections located in the zone with the highest propensity for rear-end collisions was performed on the basis of current cycle length (based on field observations). At the nine intersection where coordination is either definitely not recommended or probably not recommended, current cycle length needs to be optimized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".