A Novel Method for Acquisition of Crowdedness in City using Mobility Clustering

Detecting crowdedness spots of moving vehicles in an urban area is absolutely required to many smart city applications. The practical investigation on crowdedness spots in smart city offerings many unique features, such as highly mobile environments, the non-uniform biased samples, and limited size of sample objects. The traditional density-based clustering algorithms flop to capture the actual clustering property of objects, making the outputs meaningless. Mobility-based clustering is non-density-based approach. The basic idea is that sample objects are hired as “sensors” to recognize the vehicle crowdedness in nearby areas using their instant mobility, rather than the “object representatives”. As such the mobility of samples is certainly incorporated. Several important factors beyond the vehicle crowdedness have been identified and techniques to remunerate these effects are proposed. Furthermore, taking the identified crowdedness spots as a label of the taxi, so identify one individual taxi to be a crowdedness taxi that crosses a number of different crowdedness spots. DOI: 10.17762/ijritcc2321-8169.150613