Applying GIS-Based Traditional Travel Demand Model for Improved Network-Wide Traffic Estimation: New Brunswick Case Study
Bibliographic record
Abstract
Traffic volume counts are used by many Departments of Transportation in planning, traffic operations, and asset management programs. Traditionally, four-step model (FSM) is based on traffic analysis zones (TAZs) structure which conveniently uses existing census geography to take advantage of demographic data available from Statistics Canada. This coarse zone structure tends to exaggerate the intra-zone trips resulting in biased and unbalanced trip distribution over roadway network and high estimation errors. Also, estimation of traffic volumes on low-class roads is ignored in most cases. Limitations above have necessitated developing a GIS-based high-fidelity travel demand model (HFTDM) capable of achieving network-wide traffic volume estimation with improved accuracy. This will require using all functional class roadway network and spatially disaggregating census-based coarse TAZ structure into grid-based fine zones based on road density areal interpolation technique. Preliminary results from a case study developed for Beresford area in the Canadian Province of New Brunswick show that the proposed methodology is promising.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".