Analysis of Road User Behavior and Safety during New York City’s Summer Streets Program
Bibliographic record
Abstract
Automated computer vision video analysis techniques were used to analyze video data during the operation of New York City’s Summer Streets Program at a major signalized intersection. The main objectives of this study were to diagnose pedestrian and cyclist safety issues during the shared space operation and to demonstrate the feasibility of the automatic extraction of road user data (e.g., pedestrian, runner, rollerblader, or cyclist) required for microscopic behavior analysis. Road users’ speeds and pedestrian gait parameters (step frequency and step length) were automatically extracted and analyzed. Results show that pedestrian walking speed was highest during the Summer Streets operation (1.49 ± 0.54 m/s) because pedestrians had more street space to use and slowest during normal operations (1.30 ± 0.22 m/s). Bike speeds were low during the Summer Streets event (3.62 ± 0.97 m/s), likely because of interaction with pedestrians, but these speeds increased during normal traffic operations. Pedestrians and ...
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".