Meta-Heuristics for a Class of Demand-Responsive Transit Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The demand-adaptive systems studied in this paper attempt to offer demand-responsive services within the framework of traditional scheduled bus transportation: Users call to request service between two given points and, in so doing, induce detours in the vehicle routes; at the same time, though, a given set of compulsory stops is always served according to a predefined schedule, regardless of the current set of active requests. The model developed to select requests and determine the routing of the vehicle yields a difficult formulation but with a special structure that may be used to develop efficient algorithms. In this paper, we develop, test, and compare several solution strategies for the single line-single vehicle problem that belong to two general meta-heuristic classes, memory-enhanced greedy randomized multistart constructive procedures, and tabu search methods. Hybrid meta-heuristics combining the two methods are also analyzed.
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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 it