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Abstract
Intersections in close spatial proximity along a corridor should be considered as
correlated due to interacted traffic flows as well as similar road design and environmental
characteristics. It is critical to incorporate this spatial correlation for assessing
the true safety impacts of risk factors. In this paper, several Bayesian models were
developed to model the crash data from 170 signalized intersections in the state of
Florida. The safety impacts of risk factors such as geometric design features, traffic
control, and traffic flow characteristics were evaluated. The Poisson and Negative
Binomial Bayesian models with non-informative priors were fitted but the focus is
to incorporate spatial correlations among intersections. Two alternative models were
proposed to capture this correlation: (1) a mixed effect model in which the corridor-level
correlation is incorporated through a corridor-specific random effect and (2) a conditional
autoregressive model in which the magnitude of correlations is determined by spatial
distances among intersections. The models were compared using the Deviance Information
Criterion. The results indicate that the Poisson spatial model provides the best model
fitting. Analysis of the posterior distributions of model parameters indicated that
the size of intersection, the traffic conditions by turning movement, and the coordination
of signal phase have significant impacts on intersection safety.