A risk‐averse user equilibrium model for route choice problem in signal‐controlled networks
Bibliographic record
Abstract
Abstract This paper proposes a new risk‐averse user equilibrium (RAUE) model to estimate the distribution of traffic flows over road networks with taking account the effects of accident risks due to the conflicting traffic flows (left‐ and right‐turning and through traffic flows) at signalized intersections. It is assumed in the proposed model that drivers consider simultaneously both the travel time and accident risk in their route choices. The accident risk of a route is measured by the potential accident rate on that route. The RAUE conditions are formulated as an equivalent path‐based variational inequality problem which can be solved by a path‐based solution algorithm. It is shown that the traditional user equilibrium (UE) model is in fact a special case of the proposed model. A numerical example on a grid network is used to illustrate the application of the proposed model and to compare the results with the conventional UE traffic assignment. Numerical results show that the traditional UE model may underestimate the total system travel time and overestimate the system accident rate. Sensitivity tests are also carried out to assess the effects of drivers' preferences, signal control parameters (i.e., green time proportions), and various network demand levels on the route choice problem. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".