A comprehensive analysis of duplication in public transportation
Bibliographic record
Abstract
Route duplication is a condition in public transportation networks where multiple transit routes overlap. This occurrence can have positive effects, such as increased coverage and reliability, and negative effects, including higher operating expenses and increased network complexity. While the literature provides definitions of duplication and uses duplication indicators to analyze transit system performance, a comprehensive definition of the duplication concept is still lacking. This study addresses this gap by proposing two perspectives on duplication: route segment duplication and passenger connection duplication. We consider service frequency, the rolling stock, and the vehicle type (size and capacity) to derive the level of duplication. We also determine the subject of duplication based on two unique characteristics: trajectory (road segments and stops) and schedule (service frequency or trips). These characteristics are combined in the route variant or Line-Alternative-Direction (LAD), making LAD the subject of duplication. Our analysis indicates that the level of duplication using the proposed approach is significantly lower than that measured using traditional methods. Moreover, evaluating duplication from the service frequency perspective allows for an assessment of competitiveness among different route segments operated by various providers. A real-world case study in Almaty, Kazakhstan demonstrates the applicability of the proposed methodology to a complex urban transit network. Based on the findings, we propose policy recommendations such as shortening underutilized route segments and reallocating resources toward exclusive or high-demand links to enhance operational efficiency and service reliability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".