Introducing Weather Factor Modelling into Macro Traffic State Prediction
Bibliographic record
Abstract
Adverse weather has significant impacts on road conditions and traffic dynamics. It is observed that adverse weather as a set of exogenous factors lowers the free flow speed, shifts critical density, decreases flow capacity, and makes the freeway more prone to congestion. This paper proposes a weather factor model to be plugged into a macroscopic traffic prediction model, so that under bad weather traffic variables can be more accurately and reasonably estimated and predicted for traffic control use. To be specific, weather-specific fundamental diagrams are built by introducing weather factors to free flow speed, capacity, and critical density. The weather factors are modelled by selected weather measurements. Weather factor parameters are trained by recent historical weather and traffic data and then can be put into real-time macro traffic prediction and control. The traffic prediction model in the case study is METANET model, in which fundamental diagram parameters are one source of input. The weather-specific prediction error and conventional prediction error are compared. Real data collected by loop detectors on Whitemud Drive, Edmonton, Canada, is used for parameter calibration and prediction error evaluation. The results show that the proposed weather models reasonably improved the accuracy of macro traffic state prediction model compared to conventional model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".