Integration of public transport through a structured analysis technique
Bibliographic record
Abstract
One of the strategic objectives of the present government of South Africa is to promote the use of public transport with a goal of achieving a ratio of 80:20 between public transport and private car usage by the year 2020.However, the present structures of most cities are not conducive to the development of efficient public transport systems.Occasionally, expensive infrastructures were constructed in certain areas without proper planning.Chatsworth, a major suburban area of Durban, South Africa, has been considered as a case study in this article.The area is connected to the city centre by public transport systems, namely bus, minibus and metro rail.Surprisingly, the patronage on the rail system is very low and is decreasing.With a view to increase the accessibility to the rail system, a methodology has been presented based on data flow diagrams to develop a computer simulation model for the operational analysis of minibus taxi-rail integration.The service requirements of metro rail and the minibus taxi service were analysed based on different demand scenarios in order to determine the optimum service requirement for the integrated system.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".