A Bayesian Regression Model for Estimating Average Daily Traffic Volumes for Low Volume Roadways
Bibliographic record
Abstract
Common Average Daily Traffic (ADT) estimation models use Linear Regression and a collection of socio-economic and roadway variables. While linear regression is widely understood, it is not always optimal for developing prediction models as the regression techniques don’t have the ability to account for data distributions, or variability of the point estimates. To overcome this limitation, this paper presents a study that utilizes a Bayesian Regression model to develop a model to estimate ADT values for low volume roadways. The need for ADT estimates is critical as roadway traffic counts are the backbone of maintenance, safety and construction designs. While significant investment is made in collecting ADT values for higher functionally classified and high volume roadways, low volume roadways are often neglected in the traffic count program due to budget limitations and the misguided notion that there is limited return on investment in counting these facilities. This research developed a technique to estimate ADT for local roads in Alabama incorporating variables used in previous studies and a Bayesian Regression model. The final Bayesian Regression model relies on four independent variables: number of households in the area, employment in the area, population to job ratio and access to major roads. The model was used to generate ADT estimates on low-volume rural, local roads for 12 counties in Alabama. The paper concludes that the model can be used to predict the ADT for low-volumes roadways in Alabama for future applications.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".