Algorithm for Optimal Bid Packaging for Competitive Contracting in Public Transit
Bibliographic record
Abstract
Transit planning has traditionally been approached from the point of view of a single, public operator that dominates transit service provision. Transit market arrangements that encompass a larger potential role for the private sector in providing transit services in a competitive environment have created new opportunities for improved service efficiency and have enhanced operational sustainability. Such evolving market arrangements are bound to necessitate a transformation in the traditional transit planning approaches. Research revisited some transit planning tasks in view of evolving transit market and regulatory arrangements and investigated the implications of one such market arrangement, namely, competitive contracting, for mass transit service design. Competitive tendering elements that relate to determining the size of the contract to be tendered as well as the allocation of routes among bid packages (also known as service design) are addressed. The methodology developed in the research comprises a decision support tool for the planning of transit service tendering, with the modeling framework using a genetic algorithm–based approach to optimal bid packaging. The proposed modeling framework and case study provide a much-needed tool for the analysis of how possible moves to new market arrangements in the transit environment may achieve service sustainability objectives and reduce or eliminate the need for subsidies. The framework is useful for transit authorities wishing to contract out routes in their bus networks.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".