Path Selection and Congestion Mitigation Strategies Based on Deep Learning and Traffic Simulation Models
Bibliographic record
Abstract
Transportation demand is gradually increasing and road traffic congestion is becoming more and more serious.Traffic state prediction is one of the important bases for accurate traffic management and control.This paper investigates a traffic state prediction method based on a deep learning algorithm fusing spatio-temporal graphical convolutional networks, and explores the law of path selection decision-making of pedestrians under different traffic flow prediction and guidance strategies, and analyzes the effect of the implementation of the information guidance policy by traffic managers in realistic scenarios using evolutionary game theory.The simulation results combined with the traffic simulation model show that the traffic state prediction method proposed in this paper is more effective compared with other models.The evolution results are more reasonable when the value of the path adjustment rate in the replicated dynamic model is the inverse of the number of iterations.In the perceptual error analysis, when the value of perceptual error 1 is taken to be too large, i.e., when the perceptual error of the first type of travelers is small and small, it tends to be a deterministic choice.Finally, a traffic simulation model is implemented to validate the performance of the proposed model and propose congestion mitigation strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".