A Combined Simulation Approach to Evaluate Overtaking Behaviour on Two-Lane Two-Way Rural Roads
Bibliographic record
Abstract
A significant percentage of road fatalities and injuries occur in the nonmotorway rural road network. One of the main causes of accidents on these roads is represented by overtaking, as, by its nature, it involves a risk of a head-on collision with oncoming traffic. The paper describes a combined simulation approach (driving simulator and traffic microsimulation) designed to examine the influence of different traffic conditions on passing manoeuvres on two-lane two-way rural roads. The main focus was the evaluation of the end of the passing manoeuvre because it reflects the risk of a head-on collision. In addition, the study aimed to assess the usefulness of the proposed combined approach in the ability to proactively and quickly diagnose traffic safety problems and consequently to evaluate appropriate solutions. The data collected with an interactive driving simulator on a sample of 54 participants have been used to adjust some input data of the traffic microsimulation software. A specific situation consisting of a stationary heavy vehicle obstructing the entire lane was repeated in both experiments. The analyses focused on time-to-collision (TTC), defined as the remaining gap between the passing vehicle and the oncoming vehicle at the end of the passing manoeuvre. The results showed that the type of manoeuvre performed is significantly influenced by the traffic condition. Furthermore, the manoeuvre is influenced by the gap between two successive vehicles in the opposite lanes. Focusing on the end of the manoeuvre, it was found how a traffic increase leads to a significant reduction of the TTC values. Furthermore, the comparative analysis conducted between the data recorded following the combined approach and those obtained using exclusively the input data of the microsimulation software supports the usefulness of the proposed methodology for conducting road safety analyses, especially in complex traffic environments where drivers’ behaviour plays a decisive role.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".