Development and attribution of a linear referencing system for managing and disseminating traffic volume data on rural highway networks
Bibliographic record
Abstract
Developing and disseminating system‐wide traffic volume data are critical objectives of traffic monitoring programs. Jurisdictions commonly use maps to disseminate traffic volume data and visualize spatial traffic patterns throughout a highway network. A linear referencing system is essential in this process, yet limited research is available on the methods of sequencing (segmenting) a highway network and attributing traffic volume data to those sequences. This paper aims to fill this knowledge gap by describing the development and application of a generic three‐phase methodology: (1) to subdivide a rural highway network into sequences assumed to have uniform or homogeneous traffic volume by applying objective criteria; (2) to attribute traffic volume data to the sequenced highway network based on a set of attribution principles; and (3) to evaluate and refine the results. Application of the methodology in Manitoba resulted in the development and attribution of a new linear referencing system comprising 2,164 sequences. This provided the foundation for mapping spatial fluctuations in traffic volume at the network level. More generally, it revealed the importance of periodically evaluating the data visualization and dissemination impacts that arise when changes are made to the sampling procedure within a traffic monitoring program and when physical modifications occur on the highway network.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".