Poisson Regression: Modeling Traffic Accident Counts Based on Road Conditions and Demographics
Background
Traffic accidents are a major public safety concern, and many factors contribute to accident frequency. This study will analyze how road conditions, weather, and demographics influence accident rates using Poisson regression.
Possible Research Questions
- Do certain road conditions (e.g., wet roads, construction zones) lead to higher accident frequencies?
- How does driver age and experience impact accident counts?
- Does population density correlate with accident frequency in urban vs. rural areas?
Possible Data Sources
- National Highway Traffic Safety Administration (NHTSA) Crash Data
- Federal Highway Administration (FHWA) Traffic Volume Reports
- Local Department of Transportation Data
Key Variables
Dependent Variable: Number of accidents at a given location (count variable)
Independent Variables: Weather conditions, road type, speed limits, driver demographics, traffic volume, urban vs. rural, etc.
Methods
- Use Poisson regression to model accident frequency as a function of predictor variables.
- Check for overdispersion (considering a negative binomial model if necessary).
- Assess model goodness-of-fit using deviance and AIC/BIC criteria.