Traffic model for Copenhagen; updating of the trip matrices
Bibliographic record
Abstract
rip distribution and mode choice models, all following a utility-based framework, and network models based on mixed-Probit formulations and equilibrium formulations. The model system includes feedback cycles to take congestion into account. The behavioural functions have been estimated based on the combination of multiple Revealed and Stated Preference data-set. The presently matrices describing the 1992 travel patterns were built upon travel analyses from the end of 1980's and the beginning of 1990's. Those matrices have been adjusted to the counted traffic numerous times since the first version of the model was built in 1995. The 2004 GA-travel matrices describe travel patterns for an average working day (Monday to Friday), for five travel modes (walk, bicycle, public transport, car driver and car passenger), and six travel purposes (home-work, home-education, home-shopping, home-other private trips, non home based private trips and business trips). Day matrices were split by seven day periods. The new model operates therefore with 5 x 6 x 7 = 210 matrices. To improve the accuracy and spatial resolution it was decided to split the model analysis area from 601 zones into 818 zones while the surrounding area was split into 17 port zones. The paper describes the applied matrix estimation procedures, and demonstrates appraisals of the new matrices. The study had a fairly large budget - out of the total budget amounting to Euro 710.000, activities related to building of the travel matrices had a budget of about Euro 430.000. However, we believe that quality of modelling depends on the data foundation and the hybrid approach has been cost efficient. For the covering abstract see ITRD E135582.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".