Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data
Bibliographic record
Abstract
Summary The origin–destination (OD) matrix and the vehicle trajectory data are critical to transportation planning, design, and operation management. On the basis of the deployment of automatic vehicle identification (AVI) technology in urban traffic networks in China, this study proposed a vehicle trajectory reconstruction method for a large‐scale network by using AVI and traditional detector data. Particle filter theory was employed as the framework for this method that combines five spatial‐temporal trajectory correction factors (i.e., the path consistency, the AVI measurability criterion, the travel time consistency, the gravity flow model, and the path‐link flow matching model) to estimate the trajectory of a vehicle. The probabilities of the most likely trajectories are updated by the Bayesian method to approximate the ‘true’ trajectory. The traffic network in the Beijing Olympic Park was selected as the test bed and was simulated by using VISSIM to create a complete set of vehicle trajectories. The accuracy of the resulting trajectory reconstruction exceeds 90% when the AVI coverage is only 50%, assuming an AVI detection error of 5% for a closed network and an open network. The average relative error of a static OD matrix is 11.05%. Although the accuracy of reconstruction exceeds 80% when the AVI coverage is between 50% and 40%, the accuracy of a defective product‐OD matrix decreases rapidly. The proposed method yields high estimation accuracy for the full trajectories of individual vehicles and the OD matrix, which demonstrates significant potential for traffic‐related applications. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".