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An increasing research effort has been made on spatially disaggregated safety analysis models to meet the needs of region-level safety inspection and recently emerging transportation safety planning techniques. However, without explicitly differentiating exposure variables and risk factors, most existing studies alternate the use of crash frequency, crash rate, and crash risk to interpret model coefficients. This procedure may have resulted in the inconsistent findings in relevant studies. This study proposes a Bayesian spatial model to account for county-level variations of crash risk in Florida by explicitly controlling for exposure variables of daily vehicle miles traveled and population. A conditional autoregressive prior is specified to accommodate for the spatial autocorrelations of adjacent counties. The results show no significant difference in safety effects of risk factors on all crashes and severe crashes. Counties with higher traffic intensity and population density and a higher level of urbanization are associated with higher crash risk. Unlike arterials, freeways seem to be safer with respect to crash risk given either vehicle miles traveled or population. Increase in truck traffic volume tends to result in more severe crashes. The average travel time to work is negatively correlated with all types of crash risk. Regarding the population age cohort, the results suggest that young drivers tend to be involved in more crashes, whereas the increase in elderly population leads to fewer casualties. Finally, it is confirmed that the safety status is worse for more deprived areas with lower income and educational level and higher unemployment rate in comparison with relatively affluent areas.
Huang et al. (Fri,) studied this question.