GPS-Based Highway Performance Monitoring Performance Monitoring Using GPS: Characterization of Travel Speeds on any Roadway Segment
Bibliographic record
Abstract
Presented is a characterization of travel speed on any roadway segment based on probe vehicle position data. Most of the characterization is based position data obtained from GPS receivers because of their high precision and their increasing availability. Comparison is also made to Qualcomm’s Automatic Satellite Position Reporting (QASPR) system because of its long history (10+ years) of extensive use by the long-haul trucking industry. Described is the use of these data in conjunction with digital map representations of roadways with particular reference to ALK’s digital map database of North America. Two examples of the use of probe vehicle based GPS data to ascertain and monitor speed on roadway segments are presented. One is a demonstration of the monitoring of the speed performance of the various road segments that make up the Québec-Windsor corridor. Extensive GPS data from the first half of 2008 characterize the speed performance of the corridor by day-of-week and time-of-day. The second example also uses GPS probe vehicle data to assign a median speed , by direction, to all 31 million arcs of ALK’s digital map database of North America. Examples of that assignment are displayed in geographic bandwidth charts and a generic example of a fastest route computed based on the assigned median speeds is presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".