Developing an “intelligent” high-fidelity GIS-based travel demand model framework for improved network-wide traffic estimation
Bibliographic record
Abstract
The four-step travel demand model (FSTDM) is based on coarse rural traffic analysis zones (TAZs) which tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over the roadway network with high estimation errors. These limitations have necessitated developing a geographic information systems (GIS)-based high-fidelity travel demand model framework (HFTDMF) capable of achieving network-wide traffic volume estimation with improved model accuracy. This requires using an all functional class roadway network and enhancing the census-based coarse TAZ structure with finer-grained spatial resolution TAZs by integrating the travel demand modeling software platform, remotely-sensed images, parcel-based digital property maps, the AZTool aggregation algorithm, and areal interpolation technique. Preliminary results from the Greater Fredericton Area (GFA) showed that increasing the GFA spatial resolution from the coarsest TAZ structure at census tract (CT) level (27 CT TAZs) to the finest TAZ structure at 4252 “fine” TAZs resulted in an improvement to modeling accuracy of R2, by 0.4092 (from 0.2490 to 0.6582) and an improvement in traffic assignment coverage by 46 percentage points (from 29% to 75%).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".