Evaluating the Accuracy of Probe-Based Truck Volumes using Continuous and Short-Duration Traffic Counts
Bibliographic record
Abstract
The widespread nature of cell phones and connected vehicle navigation systems has led to the development of commercially available probe-based traffic data products. This study assesses the accuracy of annual average daily total traffic, truck traffic, medium-duty truck traffic, and heavy-duty truck traffic volumes obtained using probe-based traffic activity indices from a North American company called StreetLight Data (StL). The probe-based estimates were compared with 2019, 2020, and 2021 volumes at eleven continuous count sites and 2019 volumes at twenty-nine short-duration count (SDC) sites in the Winnipeg Metropolitan Region. The results showed reasonable agreement between the ground truth and probe-based total traffic estimates with mean absolute percent errors (MAPEs) ranging from 8.8% to 22.1% across the study years. The medium-duty truck estimates had larger errors than total traffic with MAPEs of 29.9% to 37.5%. Despite having higher volumes than medium-duty trucks, heavy-duty trucks had the smallest probe data sample and largest errors with MAPEs of 56.6% to 96.4%. Benefiting from its larger sample size, the StL medium-duty truck index was found to be a better predictor of heavy-duty truck traffic than the heavy-duty truck index. Further, the total truck volumes estimated using only the medium-duty index were more accurate than those taken as the sum of the medium and heavy-duty truck volumes obtained using their respective indices. Finally, the percent differences for the 2019 annual average daily total traffic and truck traffic estimates at the SDC sites were comparable when only the medium-duty index was used for truck volume estimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".