Safe Routes To Play? Pedestrian And Bicyclist Crashes Near Parks In The Los Angeles Region
Bibliographic record
Abstract
Background: Areas near parks may present active travelers with higher risks than in other areas due to the confluence of more pedestrians and bicyclists, younger travelers, and the potential for increased traffic volumes. This paper examines active travel crash risk near parks and builds on existing research around disparities in park access and extends research from the Safe Routes to School and Safe Routes to Transit movements to parks. Methods: We utilized the Green Visions Parks coverage, encompassing Los Angeles County and several other cities in the LA Metropolitan area. We used negative bionomial regression modeling techniques and 10 years of geolocated pedestrian and bicyclist crash data to assess the risk of active travel within a quarter mile (~400 meter) buffer around parks. We controlled for differential exposures to active travel risks using travel survey data and Bayesian smoothing models. Results: Of 1,311,736 parties involved in 608,530 crashes there were 896,359 injuries and 7,317 fatalities. The risk of crash is higher for active travelers within a quarter-mile of a park than outside that buffer, with a ratio of 1.52 per 100,000 residents compared to areas outside buffer, after adjusting for varying rates of active travel in each census tract. This higher risk near parks is amplified in neighborhoods with high proportions of minority and low-income people. Higher traffic levels are highly predictive of active crashes. Conclusions: Planners should consider the higher risks of active travel near parks and the socioeconomic modification of these risks. Additional traffic calming and safety infrastructure may be needed to provide safe routes to parks.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".