Predicting Pedestrian Crossing Behavior and Violations Using a Pathfinding Approach
Why this work is in the frame
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Bibliographic record
Abstract
Increasing walking mode share is a widespread goal, but achieving this goal requires tools to proactively evaluate potential facility designs and control strategies. Existing tools do not provide the ability to simulate a pedestrian’s localized pathfinding decisions and road crossing behaviors and model pedestrian violation behaviors. This paper proposes a pedestrian simulation model in which pedestrians follow a minimum-cost route in which costs consider the surface type, violations, and interactions with vehicles. The model is calibrated and validated using field observations at a stop-controlled intersection and a midblock crossing. Results show that the model can predict the proportion of pedestrians performing a violation behavior on their way from a given origin to destination, including the trade-offs between waiting for a gap in traffic to cross versus diverting to the nearest designated crosswalk.
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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 | 0.000 |
| 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 it