Solving the Stochastic Multiclass Traffic Assignment Problem with Asymmetric Interactions and Vehicle Restrictions
Bibliographic record
Abstract
In this paper, the authors develop a customized path-based algorithm for solving the stochastic multiclass traffic assignment problem with asymmetric interactions and vehicle restrictions. The algorithm consists of using an iterative balancing scheme to find the search direction, a self-regulated averaging (SRA) line search scheme to determine a suitable stepsize, and a column generation scheme to generate a universal path set for multiple vehicle classes. These three schemes work together in the customized path-based algorithm to solve the stochastic multiclass traffic assignment problem with considerations of asymmetric interactions among different vehicle types through the link travel time functions, route overlapping using the path-size logit (PSL) model for accounting random perceptions of network conditions in a stochastic user equilibrium (SUE) framework, and various vehicle restrictions in a transportation network. A real network in the City of Winnipeg, Canada is used to examine the computational performance of the customized path-based algorithm. In addition, sensitivity analyses are conducted to test the algorithmic effectiveness with respect to several model parameters and percentage of trucks in the transportation network. Numerical results reveal that the path-based algorithm with the SRA line search scheme is computationally effective in solving the stochastic multiclass traffic assignment problem with different modeling considerations, and also is computationally robust against various model parameters in the sensitivity analyses.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 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".