Spatial Analysis of Traffic Growth and Variations and Their Implications to the Operations of a Traffic Monitoring Program
Bibliographic record
Abstract
Highway agencies commit significant resources to traffic monitoring programs to obtain Annual Average Daily Traffic (AADT). Although many guidelines exist at the national or the provincial levels about how to best operate a traffic monitoring program, there are still quite a few steps relying on subjective judgements, such as how to apply traffic growth rate and how to assign short-term traffic counts (STTCs) to permanent traffic counts (PTCs) or permanent traffic counter groups (PTCGs). In order to reduce risks of potentially significant AADT estimation errors, PTC data from the province of Alberta, Canada are used to examine spatial correlation in traffic seasonality as well as traffic growth rate for all the road segments covered by its PTC program. The results show that roads in a functional class group can have several seasonal traffic patterns, and there is no definitive relationship between functional class and traffic seasonality. The finding indicates that the STTCs to PTCGs assignment procedure in the Federal Highway Administration 's (FHWA) method may not be appropriate, as it is based on a road's functional class only. The results also demonstrate that traffic growth rates are highly clustered in the study area, and the correlation analysis revealed that growth rates applied to short-term counting sites should be taken from the closest PTC on a road from a similar functional class. In this regard, Geographical Information Systems (GIS) analyses provide a clear portrait of how traffic growth distributes over a jurisdiction and thus reduces the judgmental errors associated with the task. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".