Cognitive Traffic Anomaly Prediction from GPS Trajectories Using Visible Outlier Indexes and Meshed Spatiotemporal Neigh
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Cognitive Traffic Anomaly Prediction from GPS Trajectories Using Visible Outlier Indexes and Meshed Spatiotemporal Neighborhoods Guang-Li Huang1
· Ke Deng1 · Jing He2
Received: 19 February 2019 / Accepted: 20 May 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The advancement of cognitive computing for traffic status understanding, powered by machine learning and data analytics, enables prediction of traffic anomalies from continuously generated big GPS trajectory data. Existing methods generally use traffic indicators such as traffic flows and speeds to detect anomalies, but they may over-identify anomalies while missing the critical ones. For example, they use historical anomalies to train the prediction model, but past anomalies may not be a perfect indication of future anomalies since anomalies are often rare. In this paper, we propose a novel cognitive approach, a Visible Outlier Indexes and Meshed Spatiotemporal Neighborhoods (VOI-MSN) method, to predict traffic anomalies from GPS trajectories. In the VOI-MSN method, two cognitive techniques are provided. The first is VOI, which measures the abnormal scores using overall samples and can be intuitively understood by humans. The second is MSN, which learns the dynamic impact range (i.e., spatiotemporal neighborhood) from historical trajectory data and provides a complete and exact analysis of the local traffic situation. It emulates human cognitive processing to adaptively judge the impact range by experience. The effectiveness of the proposed method is demonstrated using a massive trajectory dataset with 2.5 billion location records for 27,266 taxis, and it achieves higher precision and recall in predicting traffic anomalies than the counterpart methods. The VOI-MSN method achieves high accuracy and recall for predicting traffic anomalies. It outperforms traffic indicator–based (speed and traffic flow) methods, the fixed-size spatial neighborhood method and the causal network method. Keywords Cognitive anomaly prediction · Trajectories · Abnormal levels · Visible outlier indexes · Spatiotemporal neighborhoods
Introduction The advancement of cognitive computing for traffic status understanding [1, 2], powered by machine learning and data analytics, enables prediction of traffic indicators (such as traffic flows [3–6] and average speed [7]) and traffic anomalies [8–10] from continuously generated big Global
Positioning System (GPS) trajectory data [11, 12]. However, existing traffic anomaly detection/prediction methods are still low in precision [13–16], and we summarize their limitations as follows. –
Guang-Li Huang
[email protected] Ke Deng [email protected]
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Jing He [email protected] 1
School of Science, RMIT University, Melbourne, Australia
2
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, Australia
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They generally use traffic indicators such as traffic flows and speeds as the data source to detect anomalies, but it is hard to define abnormal value ranges o
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