Bilevel Decision-Support Model for Bus-Route Optimization and Accessibility Improvement for Seniors
Bibliographic record
Abstract
Bus route networks play a pivotal role in public transit system planning, which, in turn, influences the geographical distribution and service coverage of bus transit. Although some advanced approaches have been applied to optimize route network planning, transit-related social exclusion still exists for particular socially disadvantaged groups, such as seniors. Furthermore, because transit agencies are generally the primary decision makers in conventional route optimization, the leading decisions may not favor the interests of transit users. In this study, an optimization methodology for bus route redesign is introduced in order to facilitate transit operation management and enhance the benefits that seniors receive from transit services. A bilevel decision support model (BLDSM) for two schemes is formulated for the identified problem. In the proposed model, transit agencies, as lower-level decision makers, locate appropriate bus stops and generate bus routes using the shortest distance as an optimization criterion. Meanwhile, decisions with regard to providing maximum accessibility to seniors as the upper-level decision makers are taken into account. To address this problem, a location-routing-allocation strategy is proposed and implemented in Scheme I using exact methods, and, in Scheme II, exact methods are integrated with a genetic algorithm (GA) in order to identify a near-optimal solution. A numerical example is provided to assess the feasibility of the proposed method for the two schemes. A discussion of what might happen if the roles of transit agencies and seniors were adjusted in the built BLDSM is also included.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".