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We utilise collaborative path-finding to improve efficiency of smart parking systems and therefore reduce traffic congestion in metropolitan environments, while increasing efficiency and profitability of parking garages. A significant portion of traffic in urban areas is accounted for by drivers searching for an available parking space. Many cities have adopted a parking guidance and information system to try to alleviate this traffic congestion. Typically these systems entail informing the driver of the whereabouts of an available space, reserving that space for the specific driver, and providing directions to reach the destination. Little or no account is taken of how much congestion will be caused by multiple drivers being directed to the same car-park concurrently. We introduce the concept of collaborative path-finding to the problem. We simulate a smart parking system for an urban environment, and show that a novel approach to collaboratively planning paths for multiple agents can lead to reduced traffic congestion on routes toward busy parking areas, while reducing the amount of time when parking spaces are vacant, thereby increasing the revenue earned.

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In 2003, Coupland and John [2] proposed an approach to type-2 fuzzy arithmetic using the representation theorem. While this approach was sound, it was conceded to be computationally expensive and required considerable preliminary calculations. We present a more computationally viable approach which we believe will make type-2 fuzzy logic more useful in applied scenarios.