Safe Streets for All? Analyzing Infrastructural Response to Pedestrian and Cyclist Crashes in New York City, 2009–2018
Bibliographic record
Abstract
Although cycling and walking carry a host of benefits, neither the benefits nor the risks—those of injury and fatality—are equitably distributed. Although research has shown higher income and gentrified areas have better access to protected bicycle infrastructure, low-income and communities of color are overrepresented in severe injury and fatality rates among cyclists and pedestrians. This research employs temporal, spatial, and socio-economic data to study the distribution of cycling infrastructure and safety improvements in New York City between income groups and boroughs. The integration of temporal data representing pedestrian and cyclist injury and fatality, and infrastructure installation date, point toward the establishment of time trends in the relationship between traffic violence and safety investment. Socio-economic factors are analyzed to see how this relationship and access more generally are related to income. We observed that lower-income groups continue to be overrepresented in crashes across New York’s boroughs, with the exception of Manhattan, and although crash rates have fallen in the years since 2009, these gains do not improve the position of lower-income groups, which continue to experience a disproportionate share of fatalities and injuries. However, longitudinal multi-level logistic models controlling for reported pedestrian and cyclist injuries in previous years uncovered additional relationships between socio-economic status, injuries, location, and safety investment. For example, the implementation of safety improvements and speed humps are significantly related to reported injuries in previous years; this finding supports the city’s stated goals of targeting improvements to areas most in need of improved safety for vulnerable road users.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".