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A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories

Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Cano, Alberto; Lin, Jerry Chun Wei

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Authors

Asma Belhadi

Youcef Djenouri

Gautam Srivastava

Alberto Cano

Jerry Chun Wei Lin



Abstract

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase determines the individual trajectory outliers by computing the distance of each point in each trajectory, whereas the second identifies the group trajectory outliers by exploring the individual trajectory outliers using both feature selection and sliding windows strategies. A parallel version of the algorithm is also proposed using a sliding window-based GPU approach to boost the runtime performance. Extensive experiments have been carried out to thoroughly demonstrate the usefulness of our methodology on both synthetic and real trajectory databases. The results show that the GPU approach enables reaching a speed-up of 341 over the sequential algorithm on large synthetic databases. The efficiency of the proposed method to detect both individual and group trajectory outliers on a real-world taxi trajectory database is also demonstrated in comparison with baseline trajectory outlier and group detection algorithms. The results are very promising and show superiority of the proposed method both in reducing computational time and enhancing the quality of returned outliers. Finally, we prime our methodology and results for future refinement using deep learning methodologies.

Citation

Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Cano, A., & Lin, J. C. W. (2021). A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4496-4506. https://doi.org/10.1109/tits.2020.3022612

Journal Article Type Article
Acceptance Date Jul 1, 2020
Online Publication Date Sep 23, 2020
Publication Date 2021-07
Deposit Date Jul 2, 2021
Publicly Available Date Mar 28, 2024
Journal IEEE Transactions on Intelligent Transportation Systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 22
Issue 7
Pages 4496-4506
DOI https://doi.org/10.1109/tits.2020.3022612
Keywords Mechanical Engineering; Automotive Engineering; Computer Science Applications
Public URL https://uwe-repository.worktribe.com/output/7503403

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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works




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