Demand-Sensitive Candidate Route Generation Algorithm
Bibliographic record
Abstract
Demand satisfaction is a key component that reflects the quality of public transit from the passenger’s perspective, whereas cost minimization is considered a main objective of transit agencies. This trade-off between quality and cost turns transit network design into a multiobjective problem in which the interests of passengers and operators conflict. Transit network design involves the determination of various design elements, such as route alignments and stop locations, which are essential to serve transit demand within a particular area. The design of a transit network typically starts with the generation of a set of potential routes through the use of a candidate route generation algorithm. Existing route generation algorithms find the shortest path between a route’s origin and destination. Demand is aggregated without proper attention given to its pattern and distribution along the generated route. Given that demand actually is scattered along the transit route, the aggregate demand assumption is considered a major drawback of existing route generation algorithms. In an attempt to fill the highlighted gap in current practice, this paper presents a novel demand-sensitive candidate route generation algorithm that can address passenger and operator needs in a simple, objective function that aims to maximize route-level ridership. The proposed approach is well suited to small and rural communities and specialized transit services (e.g., flex route, demand responsive service) in which transit demand is dispersed.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".