An extended car-following model considering backward-looking effect: A machine learning approach
Bibliographic record
Abstract
Most car-following models have mainly focused on the effects of the lead vehicle on the target vehicle's behaviour or the driver's forward-looking effects, but not the effects of the vehicle behind the target vehicle (the following vehicle) or the driver's backward-looking effects. Therefore, this study proposes a data-driven car-following model that incorporates both backward- and forward-looking effects using a deep neural network (DNN). This model is called the “DNN with backward-looking effect (DNN-BE) model”. The DNN-BE model produced higher prediction accuracy than the DNN model with forward-looking effects only and a conventional mathematical car-following model that considers both forward- and backward-looking effects. It was found that the target vehicle is more likely to accelerate when the spacing with the following vehicle is shorter and the spacing with the lead vehicle is longer. The result of permutation importance also shows that variables related to the following vehicle are more important when the spacing with the following vehicles is shorter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".