Abstract: Crowded points or location is a crowded area with multiple number of object. Identify the crowdedness spots are required to control traffic in cities. In cities it’s very important to detect crowdedness spots. In case of moving vehicles in cities, it’s absolutely required to detect or identify the crowded areas for too many smart cities applications. While investigating practically on crowdedness spots in smart cities, it’s offering many features like highly mobile environments, the uneven biased samples and limited size of sample objects. Traditional way “density based clustering” flop to achieve actual clustering object and making the output illogical. Mobility based clustering is non-density based approach using which sample objects are hired as “sensors” to recognize crowded spots in nearby areas using mobility rather than object representation. Multiple important factors beyond the vehicle crowdedness have been identified and techniques to rewards these effects are proposed. This paper is mainly concentrate to find out how much crowdedness in an area using different methods. Technologies used to find out the crowded spots are GPS System, speedometer, radio waves etc. Different methods are used as density based clustering algorithms. Mobility based clustering, UMicro etc. Today clustering of moving object is high supporting/researching topic.
Keywords: Mobility based clustering, Data mining, traffic detection, vehicles, crowded spots, intelligent transportation system, vehicular and wireless technologies.