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Information 2018, 9(10), 258; https://doi.org/10.3390/info9100258

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 4 September 2018 / Revised: 26 September 2018 / Accepted: 11 October 2018 / Published: 20 October 2018
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Abstract

In road networks, an ‘optimal’ trajectory is a geometrically optimal drive from the source point to the destination point. In reality, the driver’s driving experience or road traffic conditions will lead to differences between the ‘actual’ trajectory and the ‘optimal’ trajectory. When the differences are excessive, these trajectories are considered as anomalous trajectories. In addition, these differences can be observed in various trajectory features, such as velocity, distance, turns, and intersections. In this paper, our aim is to fuse these trajectory features and to quantitatively describe this difference to infer anomalous trajectories. The Dempster-Shafer (D-S) evidence theory is a theory and method that uses different features as evidence to infer uncertainty. The theory does not require prior knowledge or conditional probabilities. Therefore, we propose an automatic, anomalous trajectory inference method based on the D-S evidence theory that considers driving behavior and road network constraints. To achieve this objective, we first obtain all of the ‘actual’ trajectories of drivers for different source-destination pairs in taxi Global Navigation Satellite System (GNSS) trajectories. Second, we define and extract five trajectory features: route selection ( R S ), intersection rate ( I R ), heading change rate ( HCR ) , slow point rate ( SPR ), and velocity change rate ( VCR ) . Then, different features of each trajectory are combined as evidence according to Dempster’s combinational rule. The precise probability interval of each trajectory is calculated based on the D-S evidence theory. Finally, we obtain the anomalous possibility of all real trajectories and infer anomalous trajectories whose trajectory features are significantly different from normal ones. The experimental results show that the proposed method can infer anomalous trajectories effectively and that it can be used to monitor driver behavior automatically and to discover adverse urban traffic events. View Full-Text
Keywords: anomalous trajectories; taxi GNSS trajectory data; trajectory features; Dempster-Shafer evidence theory anomalous trajectories; taxi GNSS trajectory data; trajectory features; Dempster-Shafer evidence theory
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Qin, K.; Wang, Y.; Wang, B. Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data. Information 2018, 9, 258.

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