Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study
Bibliographic record
Abstract
In recent years, an innovative public transportation (PT) mode known as the customized bus (CB) has been proposed and implemented in many cities in China to efficiently and effectively shift private car users to PT to alleviate traffic congestion and traffic-related environmental pollution. The route network design activity plays an important role in the CB operation planning process because it serves as the basis for other operation planning activities, for example, timetable development, vehicle scheduling, and crew scheduling. In this paper, according to the demand characteristics and operational purpose, a methodological framework that includes the elements of large-scale travel demand data processing and analysis, hierarchical clustering-based route origin-destination (OD) region division, route OD region pairing, and a route selection model is proposed for CB network design. Considering the operating cost and social benefits, a route selection model is proposed and a branch-and-bound-based solution method is developed. In addition, a computer-aided program is developed to analyze a real-world Beijing CB route network design problem. The results of the case study demonstrate that the current CB network of Beijing can be significantly improved, thus demonstrating the effectiveness of the proposed methodology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".