Bibliographic record
Abstract
The heterogeneity of traffic is a significant if not dominant factor in accurately modeling freeway traffic flow operations. For example, high truck percentages may induce congestion at much lower volumes, and hence different network traffic conditions may result than with low truck percentages. This implies that traffic models for real-time decision support systems in traffic management centers should provide the means to account for traffic heterogeneity. A new, multiclass, first-order traffic model is presented that provides these means and is implemented in the decision-support system BOSS-Offline, operational in all five highway traffic management centers in the Netherlands. FASTLANE differs from earlier multiclass first-order macroscopic traffic models in that it calculates the dynamics in terms of state-dependent (instead of constant) passenger-car equivalents, which is in line with both theory and empirical microscopic data. The model is numerically solved by an efficient and stable Godunov-based solver while maintaining a dynamic and realistic representation of class-specific flows and densities throughout the network. In two synthetic test cases and one based on real data, the workings of FASTLANE under different truck percentages and different conditions are demonstrated.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.060 |
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".