A Bayesian Gaussian Mixture Model for Probabilistic Modeling of Car-Following Behaviors
Bibliographic record
Abstract
Car-following models are essential for microscopic traffic simulation. While conventional models rely on parsimonious formulas with simplified assumptions, recent studies have focused on developing data-driven models with the help of high-resolution trajectory data. This paper presents a data-driven model based on a Bayesian Gaussian mixture model (GMM) for probabilistic forecasting of human car-following behaviors. By incorporating past and future information, our model captures the temporal dynamics of human car-following behaviors, providing accurate predictions of the following vehicle’s behavior and quantifying the forecast uncertainty. We demonstrate the interpretability of the Bayesian GMM in modeling car-following behaviors, providing valuable insights into the heterogeneity and uncertainty of driver behaviors. Additionally, we show that the proposed model can make probabilistic multi-vehicle simulations that reproduce natural traffic phenomena. Our results suggest that the proposed Bayesian GMM is a promising approach for modeling and forecasting car-following behaviors in various driving scenarios, contributing to the development of safer and more efficient transportation systems.
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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 | 0.000 |
| 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".