Spatio-Temporal Clustering of Traffic Networks

Venkatasubramaniam, A., Evers, L. and Ampountolas, K. (2017) Spatio-Temporal Clustering of Traffic Networks. 32nd International Workshop on Statistical Modelling (IWSM), Groningen, The Netherlands, 3-7 Jul 2017.

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.

• Item Type: Conference or Workshop Item
• Additional Information: Research funded by the Lord Kelvin Adam Smith scholarship, University of Glasgow, 2014-2018
• Keywords: Bayesian, distance-dependent Chinese restaurant process (DDCRP), network, clustering, spatial, temporal, congestion
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ampountolas, Dr Konstantinos and Evers, Dr Ludger and Venkatasubramaniam, Ashwini
• Authors: Venkatasubramaniam, A., Evers, L., and Ampountolas, K.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Infrastructure and Environment; College of Science and Engineering > School of Mathematics and Statistics > Statistics

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
65192	2	Urban Big Data Research Centre	Piyushimita Thakuriah	Economic and Social Research Council (ESRC)	ES/L011921/1	SPS - URBAN STUDIES