Spatiooral clustering of traffic data with deep embedded clustering

Abstract

Traffic data is a challenging spatiooral data, and a multivariate time series data with spatial similarities. Clustering of traffic data is a fundamental tool for various machine learning tasks including anomaly detection, missing data imputation and short term forecasting problems. In this paper, first, we formulate a spatiooral clustering problem and define temporal and spatial clusters. Then, we propose an approach for finding temporal and spatial clusters with a deep embedded clustering model. The proposed approach is examined on traffic flow data. In the analysis, we present the properties of clusters and patterns in the dataset. The analysis shows that the temporal and spatial clusters have meaningful relationships with temporal and spatial patterns in traffic data, and the clustering method effectively finds similarities in traffic data.