Influence of Zonal Configurations on Macrolevel Traffic Safety Modeling

Abstract:

Based on the historic data from Hillsborough County, Florida, U.S., the zonelevel factors including crashes counts, road network, traffic pattern, and various social economic factors were explicitly collected for four different zoning schemes, i.e. block groups, traffic analysis zones, census tracts, and zone improvement plan codes. Then, a Bayesian negative binomial model with conditional autoregressive prior was developed for each spatial units, respectively. The impacts of zonal variations on macrolevel safety modeling were investigated mainly from three aspects, i.e. model performance, model parameter estimates, as well as crash hotspots identification. Results revealed that statistical results based on different aggregation configurations could be significantly different. Zoning schemes with less number of zones tend to have higher crash prediction precision. Compared with block groups, census tracts, and zone improvement plan codes, traffic analysis zones level model preforms worst in terms of model goodness of fit. The variable of median household income shows consistently significant effects on crash frequency and is robust to variation in data aggregation.