Multifunctional Model for Traffic Flow Prediction Congestion Control in Highway Systems
Bibliographic record
Abstract
Highway transportation systems are facing challenges such as congestion due to urban development and increasing number of vehicles. This study presents a model using Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNN) to improve real-time traffic prediction and alleviate traffic congestion. The model combines LSTM networks and GNN for traffic prediction and applies multi-agent reinforcement learning (MARL) for route planning and traffic control. The study used traffic history, weather, and road network data and conducted experiments on TensorFlow, PyTorch, and SUMO platforms. The model achieved 91.8% prediction accuracy, 21.5% reduction in traffic delays and 27.0% reduction in congestion. It also reduced emergency response time by 2.3 seconds. The results of the study show that the model helps create smarter and safer transportation systems. Highway transportation systems are facing challenges such as congestion due to urban development and increasing number of vehicles. This study presents a model using Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNN) to improve real-time traffic prediction and alleviate traffic congestion. The model combines LSTM networks and GNN for traffic prediction and applies multi-agent reinforcement learning (MARL) for route planning and traffic control. The study used traffic history, weather, and road network data and conducted experiments on TensorFlow, PyTorch, and SUMO platforms. The model achieved 91.8% prediction accuracy, 21.5% reduction in traffic delays and 27.0% reduction in congestion. It also reduced emergency response time by 2.3 seconds. The results of the study show that the model helps create smarter and safer transportation systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".