Estimating temporal truck traffic patterns on rural highway systems: A spatially-aware machine learning approach
Bibliographic record
Abstract
System- or network-wide truck traffic statistics are crucial for transportation planning, infrastructure design, and managing transport networks. However, producing these statistics is more challenging than for total traffic. Current methods depend largely on engineering judgment, making them labor-intensive, susceptible to human error, and inconsistent across jurisdictions. This study aims to address these challenges by proposing a machine learning (ML) solution, utilizing Random Forest spatial classification algorithm, to automate the assignment of short-duration count stations (SCSs) to temporal truck traffic pattern groups (TTPGs) and their attribution to road segments lacking volume data. The study further focuses on identifying the key factors that influence truck traffic patterns and creating models that address data limitations. The methodology was tested using data from Manitoba's 2019 traffic monitoring program, achieving an accuracy rate of over 80 %, showcasing its potential for broader use. The proposed ML approach offers an automated, reliable, accurate, and transferable method for analyzing truck traffic patterns, reducing processing time and reliance on subjective expertise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".