Using GPS and GIS Technologies to Analyze Truck Drivers' Compliance with Traffic Regulations
Bibliographic record
Abstract
This study uses GPS and GIS technologies to analyze and compare the compliance of two populations of truck drivers with traffic signs in the City of Saint John, New Brunswick, Canada. Three types of traffic signs used in this analysis are regulatory signs, warning signs, and pedestrian signs. The criteria used to determine drivers' compliance are defined based on the Manual of Uniform Traffic Control Devices, produced by the Transportation Association of Canada (TAC 1998). With the use of GPS and GIS, the roadway network, truck speed, tracking data, and traffic sign data are integrated to obtain truck speed characteristics with respect to traffic regulations, as communicated by traffic signs. The truck speed characteristics are then analyzed, and significant factors affecting drivers driving behavior are identified. Two populations of truck drivers were analyzed for the purpose of determining the feasibility of this proposed method for performance evaluation. This study provides an effective approach for trucking firms and public agencies to identify and address safety performance issues their drivers face.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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 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".