Speed Characteristics on Manitoba’s National Highway System Roads Using Weigh-In-Motion Data
Bibliographic record
Abstract
Speed characteristics are influenced by many different factors (e.g. road engineering, vehicle classification, temporal factors, and weather factors) and in turn influence various outcomes such as safety, environmental impacts, and road user costs. In order to work towards improved outcomes in these areas, it is important to understand current speed characteristics and how these characteristics vary with influencing factors. The research leading to this paper had two objectives: (1) to synthesize existing knowledge about speed and factors that either influence speed or are an outcome of speed, and (2) to analyze speed data to determine the vehicle operating speed impacts from different vehicle classifications, temporal factors, environmental factors, and road factors. The literature review conducted for the synthesis of existing knowledge examines selected publications from the last 10 to 15 years. The speed data is obtained from the Manitoba Highway Traffic Information System (MHTIS) database from five weigh-in-motion (WIM) devices on selected portions of Manitoba's National Highway System (NHS) roads. This database contains nearly continuous year round information and approximately eight million geographically referenced speed records linked to vehicle classification and time of day for the year 2010. The results of the analysis provide a better understanding of speed behaviour under different influencing factors. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".