Modelling Dependence between Traffic Noise and Traffic Flow through An Entropy-Copula Method
Bibliographic record
Abstract
In this study, an entropy-copula method is proposed for modelling dependence between traffic volume and traffic noise on the Trans-Canada Highway (#1 highway of Canada) in the City of Regina based on a series of field experiment measurements. The proposed entropy-copula method combines the maximum entropy and copula methods into a general framework. The marginal distributions of traffic volume and traffic noise are estimated through the principle of maximum entropy (POME) theory, and the joint probabilities are derived through the Gaussian and Student t copulas. The underlying assumptions of the coupled entropy-copula method are that: i) the entropy variables are mutually independent from each other, and ii) the marginal distributions of traffic flow and traffic noise are continuous. The proposed method is applied to two field experiment sites on the Trans-Canada Highway. Based on the K-S and A-D tests and RMSE value, the entropy method shows well performance in quantifying the probability distributions of traffic volume and traffic noise. Meanwhile, both the Gaussian and Student t copulas can well model the joint probability distributions of the traffic volume and traffic noise at the both experiment sites, which is demonstrated by the Cramér von Mises statistics and the RMSE value. Furthermore, the conditional CDFs of the traffic noise at the two experiment sites are derived based on the established copulas with respect to different traffic volume scenarios. These conditional CDFs indicate positive structures between traffic volume and traffic noise at the both experiment sites. The conditional PDFs of the traffic noise under different traffic flow scenarios are also generated, indicating the potential reduction effect of traffic noise due to the decrease of the traffic volume. This proposed approach can quantify the dependence between traffic flow and traffic noise, and reveal the inherent uncertain relationship between these two variables. Moreover, the obtained results can provide useful information for traffic noise reduction through traffic flow management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".