Influence of Vehicular Flow Instability in a Transport Network on Risk Reduction: Test in a Two‐Link Network
Bibliographic record
Abstract
It is important to study risk in transport systems because mobility is subject to probable dangerous events, both internal and/or external to the traffic flow. This paper presents a method for the probabilistic assessment of social risk for endogenous traffic flow events. The model specification considers the three components of occurrence, vulnerability, and exposure. The first two components, the effects deriving from phenomena endogenous to the flow are considered and specified as a function of mean speed to consider flow instability and vehicle energy. The third component is calculated based on the fundamental flow diagram. The aim is to integrate the specified models for occurrence and vulnerability assessment and the specified models for flow instability. This paper makes a novel contribution to the field by specifying possible occurrence and vulnerability functions, for estimating the probabilities deriving from events endogenous to the flow. The methodology is applied in a two‐link road network, and the results obtained demonstrate the quality of the proposed model and the possibility of adequately modelling the phenomenon.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".