A Bayesian Linear Regression Approach to Predict Traffic Congestion
Bibliographic record
Abstract
Regression-based traffic modelling can estimate traffic congestion as a response variable by incorporating explanatory spatiotemporal components. Bayesian inference is widely used in traffic modelling as it has advantages over a frequentist approach. Previous approaches mainly focused on offsetting Bayesian inference by incorporating supervised feature extraction, data redistribution and competitive expectation-maximization techniques to achieve better accuracy in traffic forecasting. Unlike the frequentist approach, these combined Bayesian inference approaches lack interpretability. This paper proposes a simple Bayesian Linear Regression approach for spatiotemporal traffic congestion prediction that leverages Bayesian inference to facilitate model interpretability and quantify model uncertainty. The model is evaluated in terms of mean absolute error (MAE) and root mean squared error (RMSE). The experiment shows that Bayesian linear regression modelling can be trained on small data observations to quantify model uncertainty and predict traffic congestion without sacrificing interpretability and accuracy in comparison with the frequentist approach.
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 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 | 0.000 |
| 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".