Car-parking guidance with fuzzy knowledge-based decision making

This paper presents car-parking guidance with fuzzy knowledge-based decision making. The characteristic knowledge of all parking spaces is subjectively quantified via the fuzzy linguistic sets such as walking distance from parking place to building entrances, car safety, shade from sunlight outdoors, etc. With fuzzy definitions on those characteristics of parking space, the method of the ordered weight averaging can be applied to determine the truth value of the proposition: most desired characteristics of parking space are the characteristics of parking space to which the driver is being guided. The truth values of each parking space are to be used to rank all the available parking spaces. The parking space which has the maximum of the truth value is selected as the best parking space. Accordingly, the direction to the best parking space is guided in real-time by the traffic lights at intersections in parking lots for the drivers approaching. For viability of the proposed methodology, a model of real parking lots was used to simulate the interaction of the drivers to signs of traffic lights in real-time implementation.