Neighbour Corridors Travel Time Estimation: Concept and a Case Study
Why this work is in the frame
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Bibliographic record
Abstract
An approach is presented to estimate corridor travel time in urban areas using available data of a nearby corridor. The purpose is to improve the efficiency of real-time traveler information systems when the number of data collection sensors is limited. Field travel time data were collected for two corridors in downtown Vancouver, British Columbia. The association between the travel times of the two corridors was found significant. Models were then developed to estimate travel times on one corridor using data of the other corridor. The developed models included regression, Artificial Neural Network (ANN), Neuro-fuzzy, and K-Nearest Neighbours (KNN). The estimation accuracy was considered satisfactory as the Mean Absolute Percentage Error (MAPE) of all models ranged between 13.7% and 17.6%. It was concluded that the concept of estimating travel time from nearby corridors is promising. The type of modeling technique had a little impact on the results with the KNN method producing slightly better results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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