Real-time traffic incident detection using a probabilistic topic model. (December 2015)

• Record Type: Journal Article
• Authors: Kinoshita, Akira; Takasu, Atsuhiro; Adachi, Jun
• Is Part Of: Information systems. Volume 54(2015)
• Page Start: 169
• Page End: 188
• Publication Date: 2015-12
• Subjects: Anomaly detection -- Automatic incident detection -- Probabilistic topic model -- Probe-car data -- Real-time processing -- Traffic state estimation; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2015.07.002
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Traffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation–maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.