  Due to rapid expansion of urban areas in recent years, management of curbside
parking has become increasingly important. To mitigate congestion, while
meeting a city's diverse needs, performance-based pricing schemes have received
a significant amount of attention. However, several recent studies suggest
location, time-of-day, and awareness of policies are the primary factors that
drive parking decisions. In light of this, we provide an extensive data-driven
study of the spatio-temporal characteristics of curbside parking. This work
advances the understanding of where and when to set pricing policies, as well
as where to target information and incentives to drivers looking to park.
Harnessing data provided by the Seattle Department of Transportation, we
develop a Gaussian mixture model based technique to identify zones with similar
spatial parking demand as quantified by spatial autocorrelation. In support of
this technique, we introduce a metric based on the repeatability of our
Gaussian mixture model to investigate temporal consistency.
