Traffic Loading Accuracy as Affected by the Month of Short Traffic Counts
Bibliographic record
Abstract
The traffic estimates from short counts may contain a great deal of uncertainty due to the inherent traffic temporal variations. Short traffic count conducted in different seasons may result in different error values. There is no adjustment mechanism for truck type and axle weight variations in existing traffic monitoring systems. If pavements are designed based on traffic estimates from short counts, premature failures or over-design may result. This paper analyzes the impact of short traffic counts from different months on traffic loading estimates for pavement design. 48-hour vehicle classification counts from Saskatchewan highways are analyzed and ADT (average daily traffic), TP (truck percentage), and CM (content of the largest multi-trailer truck) are identified as significant parameters correlated to traffic loading ESAL (equivalent single axle load) per day. Short count samples of the three parameters are generated from continuous AVC (automated vehicle classifiers) counts to analyze the errors of short counts. For each parameter, the errors of two 48-hour counts from different months are found to be significantly lower than one count. The combined parameter error ranking for each month combination shows which month combination will provide short counts with smaller errors for the traffic parameters. Engineers should select proper time to conduct short counts. Certain flexibility can be exercised in design standard application and performance prediction.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".