Beyond traditional models: a discrete choice approach to vehicle interaction modelling at roundabouts
Bibliographic record
Abstract
This study introduces a novel application of Discrete Choice Models (DCMs) to capture microscopic vehicle-vehicle interactions at roundabouts, moving beyond rule-based microsimulation models that lack behavioural depth. The framework integrates spatial, temporal, and behavioural variables to model real-time driver decision-making. Key components include the Collision Risk Proximity Indicator (CRPI), which quantifies spatial and temporal collision likelihood, a path-following variable capturing trajectory deviations, and the Temporal Lag Car-Following (TLCF) factor for leader-follower dynamics. Using theRounD dataset, the study shows that drivers’ decisions depend on vehicle type and position of interacting vehicles. Drivers accelerate more comfortably near cars and motorcycles than heavy vehicles, which trigger defensive strategies such as creating space or adjusting speed. Drivers also decelerate when following vehicles, prioritisng safety and flow. Future directions include combining DCMs with machine learning to improve predictive accuracy and embedding them in microsimulation platforms for real-time policy and design evaluation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".