Clustering and understanding traffic flow patterns of large scale urban roads

Abstract

Classification of traffic flow patterns is important in many areas in traffic management and planning, traffic flow prediction, real-time control and datasbase design. This paper aims at clustering and modeling the traffic flow patterns from large amount of off-line traffic data. Compared with the research based on temporal context factors, urban function zones and ecologies and other geograhpical context factors complicates the analysis of this reserch. Based on the pre-classification of traffic flow data, we constructed a traffic flow similarity measurement method and applied an unsupervised learning method to achieve clustering. And then, we combine the clustering reseults with geographic context information and perform correlation analysis between them. We believe that the proposed traffic flow patterns extraction and modeling methodology, combined with the empirical analysis, help us to better understand the traffic flow patterns of large scale urban roads in modern metropolis.