Estimation of expected minimum paths in dynamic and stochastic traffic networks
Bibliographic record
Abstract
For most in-vehicle route guidance systems (RGS) currently under development, the optimal route between an origin and destination is defined as the one with the minimum expected travel time. This optimal route is calculated by applying standard shortest path algorithms to the network where the link travel times are modeled as deterministic rather than as stochastic. The drawback to this method is that while it is computationally tractable, it may, in fact, generate a sub-optimal solution. Conversely, when the stochastic nature of link travel times are explicitly modeled, an optimal algorithm can become computationally inefficient for use within an actual application. The objective of this paper is to develop a new shortest path algorithm which takes into account the stochastic nature of link travel times without significantly increasing the overall computation time. The dynamic and stochastic shortest path problem (DSSPP) is first defined and the properties associated with this problem are discussed. A heuristic algorithm based on the k-shortest path algorithm is subsequently proposed. The trade-off between solution quality and computational efficiency of the proposed algorithm will be demonstrated on a network from Edmonton, Alberta, Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".