Predicting Bus Arrival Time on the Basis of Global Positioning System Data
Why this work is in the frame
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Bibliographic record
Abstract
The ability to obtain accurate predictions of bus arrival time on a real-time basis is a vital element to both bus operations control and passenger information systems. Several studies had been devoted to this arrival time prediction problem; however, few resulted in completely satisfactory algorithms. This paper presents a new system that can be used to predict the expected bus arrival times at individual bus stops along a service route. The proposed prediction algorithm combines real-time location data from Global Positioning System receivers with average travel speeds of individual route segments, taking into account historical travel speed as well as temporal and spatial variations of traffic conditions. A geographic information system–based map-matching algorithm is used to project each received location onto the underlying transit network. The system is implemented as a finite state machine to ensure its regularity, stability, and robustness under a wide range of operating conditions. A case study on a real bus route is conducted to evaluate the performance of the proposed system in terms of prediction accuracy. The results indicate that the proposed system is capable of achieving satisfactory accuracy in predicting bus arrival times and perfect performance in predicting travel direction.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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