Heuristic Approach to Capacitated Traffic Assignment Problem for Large-Scale Transport Networks
Bibliographic record
Abstract
Although capacity constraints in traffic assignment can represent many realistic features, these constraints are largely ignored in practice because of mathematical complexities in applying the methods proposed in the literature. In this study such complexities are relaxed by the adoption of an intuitive interpretation for the Lagrange values of the capacity constraints, that is, the amount of penalty added to the travel time of the oversaturated links to discharge the excessive flow to the extent to which they become saturated. This penalty term bears some similarity to the marginal cost of the system optimal. Hence the capacitated traffic assignment problem (TAP) becomes a normal uncapacitated TAP in which the aforementioned additional penalty is updated iteratively. The proposed provision is flexible to accommodate TAP's solution algorithms such as Frank–Wolfe. The main motivation of this study is to address the needs of the industry; hence, the proposed method is coded in a leading commercial transport planning software product, and a large-scale network of Winnipeg, Manitoba, Canada, is used for numerical evaluations. Furthermore the benchmark network of Hearn is also used for comparative evaluations with respect to other methods. Results suggest that in regard to the reliability of the outcomes and computational efficacy, the proposed algorithm is as good as other methods. Unlike other methods, there is no additional parameter to be calibrated, and the convergence behavior of the algorithm is promising.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".