Decision Support in Dynamic Traffic Management
Bibliographic record
Abstract
To support operators in Regional Traffic Management Centers in their task to efficiently and safely manage traffic flows on the motorway and urban networks, a decision support system is being developed. An essential function of this system is its ability to predict the effects of a large number of candidate control scenarios, given the recurrent and non-recurrent conditions in the network. This article proposes such a prediction system referred to as BSES (Boss Scenario Evaluation System), which can evaluate control scenarios in real time, predicting their effects in terms of various measures of effectiveness, such as total travel times, vehicle loss times, average speeds, fuel consumption, etc. The main characteristics of the system are l) that it is case- based, i.e. it uses either synthetic or real-life examples of the effect of control scenarios under different circumstances; 2) that is determines the similarity of the current situation to different examples in the case-base using fuzzy logic, and 3) that it is agent-based, meaning that it predicts the effects of the different measures for small subnetworks and combines these predictions afterwards. In the article, synthetic data is used to set-up the case base. The test results described in the article illustrate the workings of the system, and shows that the system can provide the operator with real-time predictions. Furthermore, the predictions of the system are in comparable to the predictions from the simulation model used to fill the case-base, showing that the method is applicable to generalize the - in this case synthetic - data it uses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".