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Due to rapid expansion of urban areas in recent years, management of curbsideparking has become increasingly important. To mitigate congestion, whilemeeting a city's diverse needs, performance-based pricing schemes have receiveda significant amount of attention. However, several recent studies suggestlocation, time-of-day, and awareness of policies are the primary factors thatdrive parking decisions. In light of this, we provide an extensive data-drivenstudy of the spatio-temporal characteristics of curbside parking. This workadvances the understanding of where and when to set pricing policies, as wellas where to target information and incentives to drivers looking to park.Harnessing data provided by the Seattle Department of Transportation, wedevelop a Gaussian mixture model based technique to identify zones with similarspatial parking demand as quantified by spatial autocorrelation. In support ofthis technique, we introduce a metric based on the repeatability of ourGaussian mixture model to investigate temporal consistency.

Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle