  Recent crash frequency studies incorporate spatiotemporal correlations, but
these studies have two key limitations: i) none of these studies accounts for
temporal variation in model parameters; and ii) Gibbs sampler suffers from
convergence issues due to non-conjugacy. To address the first limitation, we
propose a new count data model that identifies the underlying temporal patterns
of the regression parameters while simultaneously allowing for time-varying
spatial correlation. The model is also extended to incorporate heterogeneity in
non-temporal parameters across spatial units. We tackle the second shortcoming
by deriving a Gibbs sampler that ensures conditionally conjugate posterior
updates for all model parameters. To this end, we take the advantages of
P\'olya-Gamma data augmentation and forward filtering backward sampling (FFBS)
algorithm. After validating the properties of the Gibbs sampler in a Monte
Carlo study, the advantages of the proposed specification are demonstrated in
an empirical application to uncover relationships between crash frequency
spanning across nine years and pavement characteristics. Model parameters
exhibit practically significant temporal patterns (i.e., temporal instability).
For example, the safety benefits of better pavement ride quality are estimated
to increase over time.
