Performance analysis of urban road intersections and its environmental implication: a case study of the Lagos metropolitan area
Bibliographic record
Abstract
Urban road intersections are a major factor that influences the effectiveness of traffic flow systems.This paper is aimed at examining the performance of road intersections in the Lagos metropolitan area using the "Y-junction" of Oga-Ikorodu (Lagos, Nigeria) as the case study.The study examines the delay factors, time wastage and traffic conflicts.The study uses a reconnaissance survey, delay analytical tool, vehicle spot speed study and traffic volume survey to assess the level of delay, economic loss and traffic flow interruption.The study reveals that there are varying Levels of Service (LOS) obtainable at different times of the day on different carriageways, while Ayangburen Road records the best LOS of B in the morning, the afternoon and evening recorded C and D respectively.A different scenario holds for both Lagos and Sagamu as they record the worse LOS of E in the evening peak.In a related manner, the percentage of vehicle stoppage falls between 41.1%-87.8%indicating that vehicles tend to experience a stop scenario than not-stop at each approach especially during the peaks.It is therefore pertinent to note that the road intersection operates at a very low level of service especially during the evening peak when controlled by traffic wardens.It is therefore recommended that a Diamond-at-grade intersection be constructed in the area instead of the round-a-bout.The introduction of an automated traffic control system with full control of access and total removal of on-street parking will remove the road users' burden in that area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".