Expected Time of Arrival Model for School Bus Transit Using Real-Time Global Positioning System-Based Automatic Vehicle Location Data
Bibliographic record
Abstract
The school bus is a major transportation mode for students in Canada. Unexpected delay of a school bus may be a major source of inconvenience for students and their parents. Accordingly, the provision of timely and reliable information on the expected arrivals of school buses would be of great benefit to them. This study develops an expected time of arrival (ETA) model for school buses. The model predicts arrival time from the input of two categories: the last several days' historical data and the current day's operational conditions. An operational strategy is additionally incorporated into the model to reduce the risk that an overestimated arrival time can result in missing the bus. This study evaluates the model using data collected from real-world operations of school buses on which a global positioning system-based automatic vehicle location (AVL) system is installed. The proposed model consistently shows lower levels of prediction error than moving average and regression approaches. With the operational strategy, the model provides a sufficiently reliable service in which approximately 99%–100% of students do not miss the bus, with the tolerable wait time of 162–177.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".