Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2017

Abstract

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific characteristics of vehicles trajectories (e.g., their movements are constrained by the underlying road networks), and majority of them require the input of complete trajectories. Motivated by this, we propose a vehicle outlier detection approach namely DB-TOD which is based on probabilistic model via modeling the driving behavior/preferences from the set of historical trajectories. We design outlier detection algorithms on both complete trajectory and partial one. Our probabilistic model-based approach makes detecting trajectory outlier extremely efficient while preserving the effectiveness, contributed by the relatively accurate model on driving behavior. We conduct comprehensive experiments using real datasets and the results justify both effectiveness and efficiency of our approach.

Keywords

Inverse reinforcement learning, trajectory data processing, outlier detection, driving behavior

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

CIKM '17: Proceedings of the 2017 ACM Conference on Information and Knowledge Management, Singapore, November 6-10

First Page

837

Last Page

846

ISBN

9781450349185

Identifier

10.1145/3132847.3132933

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3132847.3132933

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