Improving Traffic Flow in Emerging Cities: A SIDRA Intersection Based Traffic Signal Design
Bibliographic record
Abstract
Intersections in urban centers, especially those without any form of signalization, are accident hotspots. This, therefore, calls for ef-fective and efficient traffic management at the intersections for improved safety and efficient traffic flow. This study aimed to improve traffic flow at the Gaa-Akanbi intersection in Ilorin, Nigeria, using a traffic signal scheme. A traffic volume study and geometric features survey was carried out at the intersection. The traffic volume study was performed to determine the number, movement, and classification of vehicles at this intersection using the manual method of traffic count, while the geometric survey of the intersection was done using tape and Total Station. A 3-phase traffic signal was proposed. The optimum cycle length and signal setting were determined using SIDRA Intersection software by adopting the maximum average passenger car unit on the intersection and targeted level of service (LOS) "D". A traffic signal plan with a cycle length of 150 seconds was designed for the intersection. The amber time was considered to be 2 seconds for all phases, and green time of 48, 46 and 38 seconds was gotten for phases 1, 2 and 3, respectively; this timing ensures that minimum delay occurs at the intersection. The proposed traffic signal should be adopted at the intersection by the metropolitan traffic management agency to improve traffic management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".