Utilizing Low-Ping Frequency Vehicle Trajectory Data to Characterize Delay at Traffic Signals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Probe vehicle data is changing the landscape of transportation engineering. The availability of vehicle trajectory data, or GPS waypoint data, has expanded the utility of probe data. However, the low penetration rate of vehicles prevents signal-performance assessment during short-term or low-volume periods, such as special events, seasonal traffic patterns, and overnight timing plans. Current research has used high-ping frequency data or the temporal distributions of waypoints of less than 2 s. This paper evaluates different approaches for using low-ping frequency data to measure delays at signalized intersections. The results of statistical testing show that 30- and 60-s ping data provide delay values that are not significantly different from 1-s ping data. These sampling frequencies increase the number of observable trajectories by 700%. This data allows for scalable approaches to immediately measure delays at signalized intersections nationwide in the US, thereby reducing costly infrastructure needed for signalized performance measures.
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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 | 0.000 | 0.000 |
| 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