Data Driven Spatio-Temporal Modeling of Parking Demand

To mitigate the congestion caused by parking, 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 study of the spatio-temporal characteristics of parking demand. This work advances the understanding of where and when to set pricing policies, as well as how 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 demand as quantified by spatial autocorrelation. In support of this technique we provide a method based on the repeatability of our Gaussian mixture model to show demand for parking is consistent through time.