To mitigate congestion caused by drivers cruising in search of 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. Harnessing data provided by the Seattle Department of Transportation and considering the aforementioned decision-making factors, we analyze the spatial and temporal properties of curbside parking demand and propose methods that can improve traditional policies with straightforward modifications by advancing the understanding of where and when to administer pricing policies. Specifically, we develop a Gaussian mixture model based technique to identify zones with similar 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.


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    Title :

    Gaussian Mixture Models for Parking Demand Data


    Contributors:


    Publication date :

    2020-08-01


    Size :

    5961112 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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