Road Trafﬁc Anomaly Detection via Collaborative Path Inference from GPS Snippets
AbstractRoad trafﬁc anomaly denotes a road segment that is anomalous in terms of trafﬁc ﬂow of vehicles. Detecting road trafﬁc anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy ands parse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road trafﬁc anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred ﬁne-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxi cabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques. View Full-Text
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Wang, H.; Wen, H.; Yi, F.; Zhu, H.; Sun, L. Road Trafﬁc Anomaly Detection via Collaborative Path Inference from GPS Snippets. Sensors 2017, 17, 550.
Wang H, Wen H, Yi F, Zhu H, Sun L. Road Trafﬁc Anomaly Detection via Collaborative Path Inference from GPS Snippets. Sensors. 2017; 17(3):550.Chicago/Turabian Style
Wang, Hongtao; Wen, Hui; Yi, Feng; Zhu, Hongsong; Sun, Limin. 2017. "Road Trafﬁc Anomaly Detection via Collaborative Path Inference from GPS Snippets." Sensors 17, no. 3: 550.
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