A better understanding of the relationships between vehicle crashes and the built environment is an important step in improving crash prediction and providing sound policy recommendations that could reduce the occurrence or severity of crashes. Global statistical models are widely used to explore the relationships between vehicle crashes and the built environment, but these models do not incorporate a spatial component and are unable to deal with the issues of spatial autocorrelation and spatial non-stationarity. Our research utilizes a geographically weighted regression (GWR) model to explore the relationships between crashes and the built environment in the context of the Detroit region in Michigan. We find that the relationships between the built environment and crashes are spatially non-stationary: both the strength and the direction of their relationships differ over space. Our study also identifies several built environment variables, such as commercial use percentage, local road mileage percentage, and intersection density, that have relatively stable relationships with crashes. Our research demonstrates the feasibility and value of using spatial models in traffic, transportation, and land use research.
- Built environment
- Detroit region
- Geographically weighted regression
- Spatial non-stationarity
- Vehicle crashes