Spatio-Temporal Clustering of Traffic Networks

Abstract

We present a novel Bayesian clustering method for spatio-temporal data observed on a network and apply this model to cluster an urban traffic network. This method employs a distance dependent Chinese restaurant process (DDCRP) to provide a cluster structure, by incorporating the observed data and geographic constraints of the network. However, in order to fully capture the dependency structure of the data, a conditional auto-regressive model (CAR) is used to model the spatial dependency within each cluster