Eliminating Right-Turn-on-Red (RTOR) at Key Intersections in a City Core
Bibliographic record
Abstract
The City of Fredericton is the capital of New Brunswick, located in eastern Canada. Rightturn-on-red (RTOR) is a general practice at any traffic intersection in this maritime province. Many collisions between pedestrians and vehicles have been recorded at signalized intersections in the downtown area of the city. Due to the number of collisions, the City of Fredericton was interested in investigating how a restriction against RTOR could affect vehicular traffic. The purpose and goal of this project was to develop a calibrated traffic model of the downtown area of Fredericton that could be used for simulation studies. Two main changes were investigated: 1) a restriction against RTOR for each of eleven key intersections in the downtown area, and 2) a restriction against left-turns at one selected intersection. The traffic simulation model was also used for analyzing how factors such as pedestrian volumes, lane channelization, and turning proportions affect the changes in traffic conditions due to permitting, or prohibiting, right-turn-on-red. The traffic simulation model was created in the TSIS/CORSIM software. Several different scenarios were generated for analysis. The results of the simulation show that the traffic conditions in the whole downtown area will be affected when introducing a restriction against RTOR. Certain intersections show a relatively high change while others show no significant change at all. Several different factors were seen to affect the number of RTOR that could be performed at an intersection. One main factor was lane channelization. With a shared lane, the proportion of right-turning vehicles at the intersection was found to highly affect how many RTOR can be performed. Pedestrian volumes prove to be a third factor affecting the number of RTOR at an intersection. Overall results demonstrate that there are only a few intersections where it is suitable for the City of Fredericton to implement a restriction against RTOR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".