Citation

Wang H, Wen H, Yi F, Zhu H, Sun L. Sensors (Basel) 2017; 17(3): s17030550.

Affiliation

Beijing Key Laboratory of IOT Information Security, Institute of Information Engineering, CAS, Beijing 100093, China. sunlimin@iie.ac.cn.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17030550

PMID

28282948

Abstract

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.

Language: en

Keywords

path inference; road anomaly detection; tensor decomposition