Park hunt : an optimized approach to implement and deploy parking monitoring systems in open environments
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The time consuming, tedious, and, sometimes, never ending search for a parking spot is a matter of common experience. We present an innovative approach to parking monitoring systems that only requires sensors at the entry and exit points of a street segment in an open environment such as a city downtown (as opposed to a closed environment such as a parking garage/lot). It can be trivially understood that using this set-up we can determine the number of vehicles present in a given street segment at any given time. However, the bigger issue is to closely estimate how many of those vehicles are parked or en route. We present an algorithm by which we can have a practical estimate of parked cars without introducing any more sensors. We further present a self-stabilizing system that can be implemented for fault tolerance and a few other methods to mitigate errors that may accrue over time. Our approach is based on the assumption that drivers do not care about the exact location of the parking spot, as long as they know the “street segment” where parking is available. For example, just letting the users know of available parking on 7th street between Red River and Brazos gives them enough information to easily find a parking spot. This type of information would most likely be shown on a map. Once the driver reaches the correct street area, it is easy to locate an empty parking spot. Finally, to test and evaluate our approach, we developed and deployed an embedded system using ultra-sonic sensors, and a Microsoft Bing Map application with the said user interface, along with an interoperable web service that can provide parking information to any third party application.