Bilevel Decision-Support Model for Bus-Route Optimization and Accessibility Improvement for Seniors
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
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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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 | 0.001 | 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