Development of A Demand Model for School Trips in Colombo, Sri Lanka
Bibliographic record
Abstract
School trips share a significant portion of the traffic in peak hours in Colombo, Sri Lanka. Therefore, understanding the distribution of school trips among predefined origin and destination zones is important in managing school traffic. The applicability of the studies carried out in other countries to the Sri Lankan context is limited due to the socio-economic factors in the study area. This study mainly focused on identifying the distribution of home-to-school trips within Colombo, the capital district of Sri Lanka. A methodology is developed using home visit survey data to construct a travel O-D matrix for home-to-school trips attracted to the government schools located in the Colombo district, followed up by mathematical models to estimate the school trip distribution. The outcomes are presented using an O-D matrix for home-to-school trips followed by O-D desire lines. Further, the study uses multiple linear regression techniques to identify mathematical models to estimate the number of inter-zonal and intra-zonal school trips between origin-destination pairs in the study area. The mathematical models proposed in this research can be calibrated and used for other developing countries with similar school education systems for better estimations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".