Arrival flow profile estimation and prediction for urban arterials using license plate recognition data
Why this work is in the frame
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Bibliographic record
Abstract
Arrival flow profiles enable precise assessment of urban arterial dynamics and support signal control optimization. License plate recognition (LPR) data, with comprehensive coverage and event-based detection, are promising for reconstructing arrival flow profiles. This paper presents an arrival flow profile estimation and prediction method for urban arterials using LPR data. Unlike conventional methods assuming traffic homogeneity and ignoring wave features and signal timing impacts, our approach employs a time partition algorithm and platoon dispersion model to compute arrival flow using only boundary data. Shockwave theory defines the piecewise relation between arrival flow and profile. We further derive the link between arrival flow profiles and traffic dissipation at downstream intersections, enabling recursive estimation across all intersections. The method also predicts arrival flow profiles under various signal timing schemes. Validation through simulation and empirical cases demonstrates its robustness and reliable performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it