FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For th...

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Autores principales:	Eman O. Eldawy, Abdeltawab Hendawi, Mohammed Abdalla, Hoda M. O. Mokhtar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mining driving behavior moving objects databases outlier detection traffic condition Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/1d28c8c23fcc450391917d475996d8f4
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id	oai:doaj.org-article:1d28c8c23fcc450391917d475996d8f4
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spelling	oai:doaj.org-article:1d28c8c23fcc450391917d475996d8f42021-11-25T17:53:06ZFraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection10.3390/ijgi101107672220-9964https://doaj.org/article/1d28c8c23fcc450391917d475996d8f42021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/767https://doaj.org/toc/2220-9964Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely <i>FraudMove</i>, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed <i>FraudMove</i> system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. <i>FraudMove</i> employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows <i>FraudMove</i> to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, <i>FraudMove</i> discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of <i>FraudMove</i> in detecting outlier trajectories. The experimental results prove that <i>FraudMove</i> saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.Eman O. EldawyAbdeltawab HendawiMohammed AbdallaHoda M. O. MokhtarMDPI AGarticlemining driving behaviormoving objects databasesoutlier detectiontraffic conditionGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 767, p 767 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mining driving behavior moving objects databases outlier detection traffic condition Geography (General) G1-922
spellingShingle	mining driving behavior moving objects databases outlier detection traffic condition Geography (General) G1-922 Eman O. Eldawy Abdeltawab Hendawi Mohammed Abdalla Hoda M. O. Mokhtar FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
description	Taxicabs and rideshare cars nowadays are equipped with GPS devices that enable capturing a large volume of traces. These GPS traces represent the moving behavior of the car drivers. Indeed, the real-time discovery of fraud drivers earlier is a demand for saving the passenger’s life and money. For this purpose, this paper proposes a novel time-based system, namely <i>FraudMove</i>, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed <i>FraudMove</i> system computes the time of the most probable path of a trip. For trajectory outlier detection, a trajectory is considered an outlier trajectory if its time exceeds the time of this computed path by a specified threshold. <i>FraudMove</i> employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows <i>FraudMove</i> to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, <i>FraudMove</i> discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of <i>FraudMove</i> in detecting outlier trajectories. The experimental results prove that <i>FraudMove</i> saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.
format	article
author	Eman O. Eldawy Abdeltawab Hendawi Mohammed Abdalla Hoda M. O. Mokhtar
author_facet	Eman O. Eldawy Abdeltawab Hendawi Mohammed Abdalla Hoda M. O. Mokhtar
author_sort	Eman O. Eldawy
title	FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
title_short	FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
title_full	FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
title_fullStr	FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
title_full_unstemmed	FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection
title_sort	fraudmove: fraud drivers discovery using real-time trajectory outlier detection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1d28c8c23fcc450391917d475996d8f4
work_keys_str_mv	AT emanoeldawy fraudmovefrauddriversdiscoveryusingrealtimetrajectoryoutlierdetection AT abdeltawabhendawi fraudmovefrauddriversdiscoveryusingrealtimetrajectoryoutlierdetection AT mohammedabdalla fraudmovefrauddriversdiscoveryusingrealtimetrajectoryoutlierdetection AT hodamomokhtar fraudmovefrauddriversdiscoveryusingrealtimetrajectoryoutlierdetection
_version_	1718411848947073024

FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

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