Clustering-Based Trajectory Outlier Detection

Eldawy, Eman O.; Mokhtar, Hoda M. O.

doi:10.14569/ijacsa.2020.0110520

Cited by 6 publications

(3 citation statements)

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“…The challenge of detecting anomalous trajectories is referred to as anomalous trajectory detection [14]. Among others, we can distinguish between the following approaches to detect anomalous trajectories: clustering- [16], distance-, density-based [17], and deep-learning-based [18] anomalous trajectory detection.…”

Section: Anomalous Trajectoriesmentioning

confidence: 99%

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Rösch

Heidecker

Julian

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions -and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users -the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.

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Section: Anomalous Trajectoriesmentioning

confidence: 99%

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Rösch

Heidecker

Julian

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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show abstract

“…Ref. [28] employed the DBSCAN algorithm to complete trajectory clustering for the detection of outlier segments and outlier trajectories. The strategy of partitioning and summarization was used to build a dataset for clustering, and the angles of different line segments were adopted to determine whether a line segment would be merged into another.…”

Section: Distance-based Clustering Algorithmmentioning

confidence: 99%

Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

Long

et al. 2022

IET Intelligent Trans Sys

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The density-based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real-world scenes, and the experimental results are promising and effective.

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“…Then, those anomalies scores are ordered in decreasing order. Eldawy et al [23] proposed another method called the clustering-based trajectory outlier detection algorithm (CB-TOD). In the CB-TOD algorithm, the authors summarized the partitions of a trajectory to the smallest set of partitions without affecting the length of the original trajectory.…”

Section: Offline Trajectory Outlier Detection Algorithmsmentioning

confidence: 99%

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

Eldawy

Hendawi

Abdalla

et al. 2021

IJGI

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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 FraudMove, to discover fraud drivers in real-time by identifying outlier active trips. Mainly, the proposed FraudMove 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. FraudMove employs a tunable time window parameter to control the number of checks for detecting outlier trips. This parameter allows FraudMove to trade responsiveness with efficiency. Unlike other related works that wait until the end of a trip to indicate that it was an outlier, FraudMove discovers outlier trips instantly during the trip. Extensive experiments conducted on a real dataset confirm the efficiency and effectiveness of FraudMove in detecting outlier trajectories. The experimental results prove that FraudMove saves the response time of the outlier check process by up to 65% compared to the state-of-the-art systems.

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Clustering-Based Trajectory Outlier Detection

Cited by 6 publications

References 0 publications

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

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

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