Sensitivity analysis of traffic model zone aggregation
Bibliographic record
Abstract
It is often discussed among practitioners how many zones to apply in a traffic model. The more zones the better details in the modelling. However, more zones also require more data and more calculation time - the latter approximately increasing with the square of the number of zones. Even though computers get faster, the latter is still an issue, e.g. if more advanced stochastic user equilibrium assignment models are used. The OTM-model ofthe Copenhagen Region has been developed over a series of years. The mostrecent version has a quite detailed level with regard to the zonal structure, as it has 835 zones. The model covers the Copenhagen Region and its hinterland. The Oeresund model is a freight transport model covering the Southern part of Sweden an East Denmark. This model has 1441 zones. Finally,the European Transtools model is used, where the TEN-CONNECT version has 832 zones. The three models form a good test bed with regards to zonal structure for different geographical scales of models. The models also have arich database of traffic counts. The paper analyses the influence of the zonal structure on the results running tests by these three models. The benchmark uses the most detailed versions of the model. Based on these a method was developed to merge zones and matrices, and create new zone centroids and zone connectors from the zones to the network. Hereby, it is possible to analyse the difference of results by aggregating the zonal structurecompared to the benchmark version, as well as the changes of fits with traffic counts. Also calculation time is analyzed. Finally, it is analyzed how the models perform for project appraisal (selected projects) using different zonal structures. The study presents the results of the analyses, and try to provide some guidelines with regard to suitable aggregation of zonal structures for the three types of models; metropolitan, national/regional and international. For the covering abstract see ITRD E145999
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".