An Optimization Model for Bus Route Redesign Considering Accessibility Improvement for Seniors
Bibliographic record
Abstract
Bus transit acts as a significant catalyst in the process of sustainable and resilient urbanization. However, within any given urban agglomeration, residents do not benefit from the transit services on an equal footing. This is particularly the case for socially disadvantaged groups such as seniors, children, and low-income households. Generally, a walking distance of 400 m is used as a walkable distance threshold for bus stops in public transit design practice and age-friendly city guidelines. Unfortunately, due to irregular transit service regions or a dispersed distribution of bus stops, age-restricted communities which have become a popular residential option for older adults are not necessarily built in locations meeting the 400 m-criterion and thus provide limited accessibility to nearby bus stops. In this context, this paper aims to apply integer linear programming (ILP) to help redesign the existing bus route (or route segment) surrounding the locations of age-restricted communities in order to minimize the operation cost for transit agencies under a series of constraints including satisfying accessibility requirement of bus transit to seniors. Finally, a numerical example, based on the grid street pattern, is illustrated to demonstrate the applicability of the proposed method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".