Next Article in Journal
The 3-Omega Method for the Measurement of Fouling Thickness, the Liquid Flow Rate, and Surface Contact
Next Article in Special Issue
Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
Previous Article in Journal
Ratiometric Dissolved Oxygen Sensors Based on Ruthenium Complex Doped with Silver Nanoparticles
Previous Article in Special Issue
Coalition Game-Based Secure and Effective Clustering Communication in Vehicular Cyber-Physical System (VCPS)
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 550;

Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

Beijing Key Laboratory of IOT Information Security, Institute of Information Engineering, CAS, Beijing 100093, China
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun, Zhipeng Cai and Antonio Jara
Received: 8 December 2016 / Revised: 15 February 2017 / Accepted: 23 February 2017 / Published: 9 March 2017
Full-Text   |   PDF [10230 KB, uploaded 10 March 2017]   |  


Road 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
Keywords: road anomaly detection; path inference; tensor decomposition road anomaly detection; path inference; tensor decomposition

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top