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 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 | 0.001 |
| 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".