On detection of emerging anomalous traffic patterns using GPS data

Authors:
Linsey Xiaolin Pang;Sanjay Chawla;Wei Liu;Yu Zheng
Affiliations:
School of Information Technologies, University of Sydney, Australia and NICTA, Sydney, Australia;School of Information Technologies, University of Sydney, Australia;Dept. of Computer Science and Software Engineering, University of Melbourne, Australia;Microsoft Research Asia
Venue:
Data & Knowledge Engineering
Year:
2013

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Abstract

The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly 24h a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this article, we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area, which has the potential to estimate and improve traffic conditions in advance. We adapt likelihood ratio test statistic (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior.