Options for enhancing network-wide annual average daily truck volume estimates
Bibliographic record
Abstract
This research presents two projects that investigate options for enhancing network-wide annual average daily truck traffic volume estimates. Annual average daily traffic (AADT) and annual average daily truck traffic (AADTT) are valuable traffic statistics that are required for safety, planning, operation, design, and environmental applications. However, it is challenging to obtain estimates of AADT and AADTT across Canada’s vast highway network due to resource limitations. It is even more difficult to obtain annual average estimates of traffic volumes by specific classes of trucks, which are used for applications such as mechanistic-empirical pavement design, bridge evaluation, and asset management. The first research project investigates how annual average daily truck volumes can be enhanced by improving traffic count sampling strategies and technologies. To help determine the count duration needed to obtain a sufficient sample of vehicle volumes by class, the study uses continuous classification count data to simulate short-duration counts of 1 to 8 days. The change in the variability of the simulated count volumes with duration was used as an indicator of the accuracy of the AADTT estimates they would produce. The results showed that at most sites, a 7-day count of trucks could reach the same level of variability as a 1-day count of total traffic. In terms of reductions in count variability, there tended to be diminishing returns beyond a 2-day or 3-day count of total traffic and beyond a 6-day count of truck traffic. The second research project assesses the use of commercially available probe-based data products for truck volume estimation. The study evaluates the accuracy of total traffic and truck traffic estimates obtained from the third-party data provider StreetLight Data by comparing them to volume estimates from conventional traffic counts. This work contributes new knowledge by being the first evaluation of StreetLight Data’s medium-duty and heavy-duty truck indices in North America. The findings indicated that the probe-based AADT and heavy-duty AADTT estimates had the highest and lowest accuracy, respectively. Further, it was found that probe-based AADT and AADTT estimates were reasonably similar to the estimates obtained from short-duration counts with mean absolute percent differences of approximately 25%.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".