Application of clustering algorithms for spatio-temporal analysis of urban traffic data

Abstract

The large vehicle movement traffic datasets offer a lot of great opportunities for the evolution of new methodologies for the analysis of the transportation system. However, deriving relevant traffic patterns from such a vast amount of historical dataset is challenging. In this paper, several data mining techniques have been applied to obtain more understanding about urban traffic patterns by analyzing hourly and daily variation in urban traffic flow dataset. A model has been developed for the analysis of spatial and temporal patterns in urban traffic data. Model development involves the formulation of algorithms to be applied to the data and choice of various metrics to evaluate the clustering algorithm. Furthermore, these techniques have been applied to the traffic dataset of Aarhus, the second-largest city of Denmark. Finally, results are analyzed to determine the various factors that affect the traffic flow patterns in an urban area.