Queue Length Estimation on Urban Signalized Intersection Combining Automatic Vehicle Identification and Vehicle Trajectory Data
Bibliographic record
Abstract
Queue length is one of the indicators of the state of traffic and is often used to measure the operational state of signalized intersections. Many studies have proposed estimating queue length from vehicle trajectory data (e.g., floating car GPS data); however, its sparse spatio-temporal distribution and low sampling frequency present substantial challenges in practice. In some jurisdictions, the widespread deployment of automatic vehicle identification (AVI) technologies presents the opportunity to improve queue length estimation at signalized intersections by combining AVI and trajectory data from floating (probe) vehicles. The method proposed in this paper is applicable for both under and oversaturated traffic conditions, is evaluated using field data [Next Generation Simulation (NGSIM) data set] and simulation data, and is compared to ground truth and the method proposed by the author Tan. The results from the field data evaluation indicate that the method provides a good estimation of the queue size (mean average error less than three vehicles for a floating vehicle penetration rate of 5% and a GPS sampling interval of 10 s). The simulation data evaluation indicated that the proposed method performs better than the Tan’s method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".