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

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 DBSCAN to derive Adjusted Rand Index (ARI) matrices. Finally, these matrices are then systematically used to find similar patterns of different temporal perspectives. Multiple analyses are carried out with real data from Melbourne, Australia. Dissimilarities with intensities of up to 80% are detected that are not detectable with general clustering approaches.

Automated summary

Multiple pattern analyses of traffic data have been conducted previously, but this study introduces a hybrid method that considers temporal factors to measure the intensity of differences among various temporal factors' data. The proposed method can efficiently process historical data, denoise it with basis splines, reshape it into a 2-D latent space using PCA, and then apply pairwise K-means clustering and DBSCAN for anomaly elimination. By using Adjusted Rand Index matrices, similar patterns of different temporal perspectives can be systematically identified. Real data from Melbourne, Australia were used for multiple analyses, detecting dissimilarities with intensities of up to 80% that cannot be detected using general clustering approaches.