Intelligent Traffic Light Control System with Priority Lane Intervention
Bibliographic record
Abstract
The current traffic light system uses a lot of fixed time signals. Fixed time signals cause traffic jams in one or several lanes due to traffic congestion. On the other hand, emergency vehicles cannot get their rights to the maximum due to the density of these vehicles. This study aims to build an intelligent traffic light with a vehicle density sensor device and a smartphone application to increase the accuracy of light timing and parse traffic density. Furthermore, in an emergency, they can still overcome and accelerate the speed of passing vehicles by manually intervening with the traffic light through a smartphone application connected wirelessly to the traffic light. The intelligent traffic light is made with Arduino Bluetooth Control (ABC), Bluetooth HC-05, Arduino Mega 2560, and infrared (IR) sensor E18-D80NK. The results show that the green light setting is based on the input from the IR sensor both near sensor (NS) and far sensor (FS) work well. When NS detects a vehicle with a green light, it increases Y seconds from the default X seconds. If FS detects a vehicle with a green light, it increases by Z seconds. Settings with the ABC application can randomly turn on the green light in four lanes according to the will of the smartphone operator.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".