Analysis of traffic state variation patterns for urban road network based on spectral clustering

The traffic state evolution of urban road network is complicated and varies significantly with different roads, functional zones, and social activities. Considering the regularity of human travel activities, from a long-term perspective, typical traffic state variation patterns for road network could be extracted. In order to extract traffic variation features, spectral clustering technique, an unsupervised learning method, is applied to analyze daily traffic state variation for the region road network based on section-based traffic speed dataset. The proposed method transforms traditional clustering problems into graph partition problems, which is suitable for the clustering problems with multiple attributes by dimension reduction. In this study, five daily traffic state variation clusters are efficiently grouped with different regularities and ranges. The frequency distributions of the sections in each cluster are related with hierarchies, locations, and functions of roads. Long-term heavy-traffic road sections and abnormal traffic state caused by festivals are detected based on the analysis of clusters. The knowledge on the spatiotemporal diversity, similarity, and relativity for network traffic state variation can be naturally discovered. Traffic variation patterns could be incorporated into network-level traffic-prediction and route-guidance algorithms in intelligent transportation systems.