Traffic Conflict Prediction at Signal Cycle Level Using Bayesian Optimized Machine Learning Approaches
Bibliographic record
Abstract
This study develops non-parametric models to predict traffic conflicts at signalized intersections at the signal cycle level using machine learning approaches. Three different datasets were collected, one from Surrey, Canada, and the other two from Los Angeles and Georgia, U.S.A. From the datasets, traffic conflicts measured by modified time to collision and traffic parameters such as traffic volume, shockwave area, platoon ratio, and shockwave speed were extracted. Multilayer perceptron (MLP), support vector regression (SVR), and random forest (RF) models were developed based on the Surrey dataset, and the Bayesian optimization approach was adopted to optimize the model hyperparameters. The optimized models were applied to the Los Angeles and Georgia datasets to test their transferability, and they were also compared to a traditional safety performance function (SPF) developed using negative binominal regression. The results show that all the three Bayesian optimized machine learning models have high predictive accuracy and acceptable transferability, and the MLP model is a little better than the SVR and RF models. In addition, all three models outperform the traditional SPF with regard to predictive accuracy. The model sensitivity analysis also show that the traffic volume and shockwave area have positive effects on traffic conflicts, while the platoon ratio has negative effects.
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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 | 0.001 |
| 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".