Improving the Efficiency of Dynamic Traffic Assignment Through Computational Methods Based on Combinatorial Algorithm
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 dynamic traffic assignment (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. In addition to the “warm start” approach, parallel computing was also applied to the CDTA model to gain computational efficiency. The results are encouraging, and showed improved run times using multiple processors. Larger networks are better at realizing the full benefit of high number of processors.
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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 | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".