Traffic state estimation and prediction based on Bayesian approach in urban road networks using AVI and floating vehicle data
Bibliographic record
Abstract
Accurate estimation and prediction of traffic state is crucial for the development of intelligent transportation systems. However, existing studies focus on traffic state at the links or intersections, and under-saturated scenarios, limiting their applicability at the network. This study proposes a framework for network-scale traffic state estimation and prediction by integrating trajectory and AVI data. The framework includes queue length estimation for different links equipped with AVI systems and state estimation and prediction for unobserved links. Validation uses large-scale real-world and simulation datasets. Results show that, with real-world data, the MAE for queue length and travel time estimation are less than 0.69 vehicles and 1.35 s, respectively, with prediction MAE around 1 vehicle. In simulations, the proposed method outperforms benchmarks under various demands, achieving queue length MAE of 2.43 vehicles under high demand. These findings indicate high accuracy in both estimation and prediction, suitable for under-saturated and over-saturated conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".