Vehicle stacking estimation at signalized intersections with unmanned aerial systems
Bibliographic record
Abstract
Fleet composition and vehicle spacing on roads are important inputs to mobile source emission models and traffic planning. In this paper, we present a novel method that employs an unmanned aerial system (UAS) to capture imagery of stationary vehicle formations at two different intersections and times of the day. The imagery is processed through photogrammetric software to generate 3-dimensional (3D) models of the formations allowing for measurement of the stacking gaps and identification of individual vehicle types for fleet composition evaluation. Statistical tests were performed on the different flight results to assess traffic behavior (both composition and gaps) were similar and can be pooled. In both cases, the variation of fleet composition and gaps were similar. However, the stationary headway gaps followed a logarithmic distribution and had to be transformed after pooling. The final results of the fleet composition measured varied significantly from the estimated mix based on registered vehicles, while the average vehicle spacing was approximately 2.17 m and did not depend on vehicle type, location or time of day. These results were used to prepare a Monte Carlo Analysis model to estimate the total number and types of vehicles on a 1 km road section. The model was extended from stationary traffic to traffic moving up to 20 km/h by assuming a linear increase of the spacing gap. This research paper is one of the first of its kind to study the stacking spaces of mixed fleets at signalized intersections and shows that spacing is dependent more on individual driver behavior than vehicle type.
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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 | 0.001 |
| 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) | 0.001 | 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".