Integrating Statistical Modeling and Public Policy: Temporal and Environmental Predictors of Fatal Road Crashes in New York City
Authors
Matthew Shang
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Traffic crashes remain a major source of preventable urban mortality. This study applied a two-stage statistical framework to 2023 New York City crash data to assess temporal and environmental predictors of fatality. Crashes peaked during evening hours, with the highest fatality risk between 20:00 and 23:00. Fatal crash odds increased under dark, unlit conditions (adjusted odds ratio [aOR ≈ 2.10]), rain (aOR ≈ 1.35), head-on collisions (aOR ≈ 3.25), and single-vehicle incidents (aOR ≈ 1.85). Results reveal measurable temporal and environmental patterns in fatal risk, supporting statistical modeling as a foundation for data-driven, policy-oriented safety interventions.
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Authors
Matthew Shang
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References:
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