Bibliographic record
Abstract
For trajectory-based travel time prediction model, map matching shows its excellence in terms of GPS data processing by providing an efficient technique to generate the vehicle trajectory on the digital map. The transit vehicle trajectory contains essential information about arrival time at bus stops and delay at major intersections. An understanding of reliable map-matching method is necessary for the development of the accuracy of real-time prediction result accuracys. This thesis provides an enhanced map-matching method, which has better performance in terms of accuracy ofinferred path inference and link identification accuracy, compared with Spatial-temporal matching method, a well-recognized map-matching method used in previous literature. Compared with the existing map-matching method, a reference point file is added to originalthe digital map, converting the point-to-curve match to point-to-point match. The map is also divided into equal digital grids by latitude and longitude to narrow down the matching scale. The feasibility and the accuracy of the method are evaluated in different traffic environment using real field geometric information and GPS data. The last part of the thesis will beis the comparison analysis of between single transit trajectory predictionprediction results using the matching resultsderived from from both map-matching methods,. The field test which is conducted on 23rd Avenue corridor from Legar transit center to Century Park transit center in Edmonton.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".