Temporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method

Abstract

Multiple pattern analyses of traffic data have been conducted previously; however, it has yet to be explored with an awareness of temporal factors in big real-world traffic data. In this paper, we introduce a hybrid method to measure the intensity of differences among various temporal factors’ data. The proposed method can efficiently process the historical data given temporal factors and provide insightful information about the intensity of variations. After data denoising with basis splines, we reshape the time series into a 2-D latent space using Principal Component Analysis (PCA) according to the type of analysis. Pairwise K-means clustering is then applied after anomaly elimination with..