Expected Time of Arrival Model for School Bus Transit Using Real-Time Global Positioning System-Based Automatic Vehicle Location Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it