Adaptive Traffic Lights with Selective Use of Detectors for Reducing Congestion at Urban Intersections
Bibliographic record
Abstract
Population growth and the increasing demand for urban mobility have led to a rise in the number of vehicles and traffic congestion in cities such as Lima, particularly at intersections in the Surco district.This situation not only causes delays but also poses risks to pedestrian safety.The current static traffic light synchronization does not adapt to variable traffic conditions, resulting in time losses and high economic costs.This study addresses the design and simulation of an adaptive traffic light system for an intersection, using VISSIM software and strategically positioning detectors in the most critical lanes.The main objective is to improve congestion indicators such as delays, queue lengths, and the level of service.The contributions include data collection of the current situation, simulation and validation of this scenario, programming of adaptive traffic lights in VisVAP, and a comparative analysis of results.The findings show considerable improvements, such as a reduction in delays between 19.0% and 23.0%, a 42.3% decrease in queue lengths, and an improvement in service levels to categories A and B, effectively eliminating the lowest service levels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".