A combinatorial algorithm and warm start method for dynamic traffic assignment
Bibliographic record
Abstract
A new combinatorial dynamic traffic assignment (CDTA) algorithm for multi-destination transportation networks is developed. The algorithm, stated on a discrete space-time network, uses the cell transmission model (CTM) to propagate traffic, thereby ensuring that traffic dynamics such as queue evolution, link spill-over, and shockwave propagation are adequately captured. The CDTA algorithm assigns vehicles to optimal time-dependent shortest paths in one shot by finding connectivity between origin-destination pairs in the time-expanded CTM network. The CDTA algorithm runs in polynomial time and is guaranteed to find a user optimal assignment in single-destination networks. However, vehicular trajectories could potentially violate first-in first-out (FIFO) condition in a multi-destination network (thereby yielding an optimal though infeasible solution). FIFO flows are achieved by simulating the vehicular trajectories using a simulation-based DTA model. These flows in turn serve as an initial feasible DTA solution — this method is called “warm starting” a simulation-based DTA model. The algorithm has been tested for the Anaheim and Winnipeg networks for varying demand levels. The warm started DTA models performed better than the non-warm started models in terms of equilibrium convergence metrics. In particular, for solutions involving small path sets, the DTA model warm started using the CDTA algorithm provided better solutions than the purely simulation-based model.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".