  Modelling crash rates in an urban area requires a swathe of data regarding
historical and prevailing traffic volumes and crash events and characteristics.
Provided that the traffic volume of urban networks is largely defined by
typical work and school commute patterns, crash rates can be determined with a
reasonable degree of accuracy. However, this process becomes more complicated
for an area that is frequently subject to peaks and troughs in traffic volume
and crash events owing to exogenous events (for example, extreme weather)
rather than typical commute patterns. One such area that is particularly
exposed to exogenous events is Washington, DC, which has seen a large rise in
crash events between 2009 and 2020. In this study, we adopt a forecasting model
that embeds heterogeneity and temporal instability in its estimates in order to
improve upon forecasting models currently used in transportation and road
safety research. Specifically, we introduce a stochastic volatility model that
aims to capture the nuances associated with crash rates in Washington, DC. We
determine that this model can outperform conventional forecasting models, but
it does not perform well in light of the unique travel patterns exhibited
throughout the COVID-19 pandemic. Nevertheless, its adaptability to the
idiosyncrasies of Washington, DC crash rates demonstrates its ability to
accurately simulate localised crash rates processes, which can be further
adapted in public policy contexts to form road safety targets.
