The using of 3-dimensional modelling for the calculation of regional transport demand matrix
Bibliographic record
Abstract
The traffic demand matrix belongs to the most important data for a traffic impact assessment and proposal of measures in a crucially stressed area. This matrix can give an answer to the question from where and to where people travel for different purposes and it helps to identify relations between traffic origins and destinations. The matrix either can be deduced from a directional traffic census (car transport matrix) or calculated with the help of trip distribution processes. The 2-dimensional trip distribution is the most common method. In this case, a traffic production and attraction as well as a function of distance or travel time between all zone pairs are used as input data. The paper will present less used trip distribution method - 3-dimensional modelling - in Middle Bohemian Region, where transport demand between districts known from Czech National Population Census has been used as a 3rd dimension. This 3rd dimension better take into account that the demand inside a district is stronger than inter - district demand. The modelling has been performing by Canadian EMME/2 software and it enables the generation of more realistic transport demand matrices for certain trip type (commuting). The resulted matrix is a fundament for next processes: model network assignment and calculation of traffic volumes and emissions, creation of model development scenarios and evaluation of planned constructions and measures from the viewpoint of its environmental impacts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 0.002 |
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".