Calibration of Route Choice Preferences and Dynamic Traffic Assignment Model in China Using Automated Vehicle Identification Data
Bibliographic record
Abstract
Automated vehicle identification (AVI) data has been prevalent in China due to high coverage of policy camera. Longitudinal AVI data has the potential to help interpret commuters’ route choice behavior and further help to build a dynamic traffic assignment (DTA) model of large-scale road networks. With the help of DTA model, we can understand the influence of the evolution of route choice preference on traffic operation efficiency. In this study, we adopted the finest AVI data set so far in Shanghai, China, and performed route choice preference classification. With AVI trajectories and multi-source traffic data, a DynusT DTA model was built and calibrated from the both sides of supply and demand. Then we investigated the impact of route choice preferences. It is found that if all commuters followed suggestions of user equilibrium route choice, the total travel time across the entire network would be saved by around 35%, which is significant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".