Identifying Regions of High Demand for Transportation Services based on Cluster Evolution and Graph Analysis

Identifying regions of high demand for transportation services can help drivers maximize their profits, assist companies in dynamic pricing or resource allocation, and reduce passenger’s wait times. However, their identification is not trivial because of the many factors that impact the demand, such as the weather or time of the day, and the possible lack of communication between drivers. In this paper, we present a framework to identify regions of high demand for transportation services based on cluster evolution and graph analysis. The framework identifies how clusters of moving objects evolve, creates a graph to represent the evolution, and use cluster relationships to calculate a rate of change based on the objects that enter of leave the cluster. Results can be described based on the day or the evolution of a cluster. A use case with taxis in Rome is performed and two main regions of high demand for taxis are identified, one near hotels and another near a bus, taxi, and train terminal.