Better Safety Analyses through Smarter Data: Adding Open-Street-View and Traffic-Calibrated Location-Based Services Data to Pedestrian Crash Analysis in Lincoln, NE
Bibliographic record
Abstract
Pedestrian crashes are a significant concern in the U.S., with pedestrian fatalities increasing and outpacing those of vehicle occupants. This research investigates the potential of new data sources to enhance pedestrian safety analysis and crash modeling. Specifically, it examines the use of StreetLight-calibrated traffic volumes and Mapillary detections of street objects for modeling pedestrian crash counts and severity. By integrating these innovative data sources, the study aims to improve the accuracy and granularity of safety evaluations. Both generalized linear models and machine learning (ML) models, including random forests (RF) and gradient boosting machines, demonstrated acceptable performance and solid portrayal of crash dynamics, with ML models providing better predictive power at the cost of complexity and lower interpretability. Additionally, the weighted RF classifier showed high accuracy in predicting crash severity. Key variables in our analysis encompassed StreetLight volumes and various Mapillary open street-view detections, including traffic signals, crosswalks, advertisement signs, store signs, streetlights, and arrow markings. The association between these variables and crash counts and severity aligns with our understanding of crash patterns. Overall, the research underscores the importance of leveraging detailed, real-world data to improve pedestrian safety analyses and contribute to more effective safety strategies and policy decisions.
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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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".