Improving Group Assignment and AADT Estimation Accuracy of Short-term Traffic Counts using Historical Seasonal Patterns & Bayesian Statistics
Bibliographic record
Abstract
Annual Average Daily Traffic (AADT) is one of the most important pieces of data being used widely in planning, design, operation and management of roads and facilities. A reliable estimate of AADT has always been one of the main interests of transportation and highway agencies. Traditionally, AADTs are estimated for the majority of road segments of a network from short-term traffic counts (STTCs or coverage counts) by applying a set of expansion factors derived from permanent traffic counts (PTCs). Literature indicates that the FHWA (Federal Highway Administration) method may be most widely used. In this method, roads in the same functional class are assumed to have similar traffic patterns, and the factors derived from PTC data collected from the class is used to convert STTCs to AADT estimates. However, it should be noted that, because roads from a functional class do not necessarily have similar seasonal traffic variations, this method may sometimes produce large estimation errors. In this regard, this paper proposes a novel pattern-matching method, which constructs a seasonal traffic variation profile for a short-term counting site using all historical counts available and then use this profile to assign the site to a PTC or a PTC group. In addition, a Bayesian approach is developed to explicitly consider and show the “risk or uncertainty” associated with assigning each short-term counting site to different PTC or PTC groups. Study results based on the simulated STTCs from a permanent counter on a winter recreational road in Alberta, Canada show that the new method proposed can limit the 95th percentile (P95) AADT estimation error to less than 13%, in contrast to 21.7% from the FHWA method. Moreover, it should be noted that the proposed method will not impose any additional monitoring cost, or make any change to existing traffic monitoring programs, and therefore it will be easy for highway agencies to incorporate the proposed method in their practice.
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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 | 0.000 | 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 | 0.000 |
| 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".