Time-Dependent Popular Routes Based Trajectory Outlier Detection

Zhu, Jianping; Jiang, Wei; Liu, An; Liu, Guanfeng; Zhao, Lei

doi:10.1007/978-3-319-26190-4_2

Cited by 40 publications

(22 citation statements)

References 19 publications

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“…There exist many research directions for the trajectory data. Currently, researchers are focusing on optimizing effective trajectory indexing structures [11], and developing methods for trajectory frequent pattern based on grid sequence [12]- [14], trajectory outlier detection based on trajectory information entropy distribution [15], abnormal trajectory detection for intelligent transport system [16], trajectory uncertainty management [17], [18], and mining knowledge from trajectory data [19], [20], etc. Among these domains, the study of abnormal trajectories is an important research direction.…”

Section: Related Workmentioning

confidence: 99%

Abnormal Trajectory Detection Based on a Sparse Subgraph

et al. 2020

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Traditional abnormal trajectory detection algorithms mainly involve the measurement of a single feature; however, the influence of other features on abnormal trajectory is ignored, resulting in the inability to fully discover the abnormal trajectory in the trajectory database. To overcome this limitation, we propose an abnormal trajectory detection method-called TADSS-to find the hidden abnormal trajectory by using a comprehensive measurement. Firstly, we employ three kernel functions to measure the time, velocity and position feature values of trajectory data, where the kernel functions extract semantic feature of the position, time feature of trajectory, and velocity feature of object motion from each trajectory data. Secondly, we propose a feature fusion strategy to measure the similarity of trajectory data, where we assign weights to the above kernel functions and then use a linear combination approach to fuse the weighted kernel functions. Thirdly, we build a trajectory feature graph by using the above fused kernel functions, and then divide the trajectory feature graph into a plurality of subgraphs by using a conventional graph clustering technique. Last, we propose a sparse subgraph method to detect abnormal trajectory, where a novel weight coefficient concept is used to distinguish sparse subgraph. Experimental results driven by both the vehicle trajectory data of Shanghai city and the Atlantic hurricane data demonstrate the performance of our TADSS.

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Section: Related Workmentioning

confidence: 99%

Abnormal Trajectory Detection Based on a Sparse Subgraph

et al. 2020

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“…Specifically, in order to process trajectories online and find the anomalous at early stag, IBOAT builds an inverted index for historical trajectory data. [26] proposes a time-dependent popular routes based algorithm which takes spatial and temporal abnormalities into consideration simultaneously to improve the accuracy of the detection. [20] proposes a probabilistic model-based approach via modeling the driving behavior/preferences from the set of historical trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, automatically anomalous trajectory detection has attracted extensive research attention. Some existing anomalous trajectory detection methods have been proposed to deal with this problem [13,2,8,23,4,26,20] . [13] proposes a partition-and-detect framework for anomalous trajectory detection, which partitions a trajectory into a set of line segments and detects outlying line segments for trajectory outliers based on distance and density.…”

Section: Introductionmentioning

confidence: 99%

“…IBAT [23] and the augmented version IBOAT [4] present the isolation-based anomalous trajectory detection methods which detect anomalous by comparing a new trajectory against a large collection of historical trajectories. [26] determines whether an input trajectory is an anomalous by taking spatial and temporal abnormalities into consideration simultaneously. However, most existing methods addressing the anomalous detection problem are only based on density or isolation methods.…”

Section: Introductionmentioning

confidence: 99%

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Anomalous Trajectory Detection using Recurrent Neural Network

Wang

Ding

et al. 2018

EasyChair Preprints

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Anomalous trajectory detection which plays an important role in taxi fraud detection and trajectory data preprocessing is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods which utilize density and isolation approaches mainly focus on the differences of a new trajectory and the historical trajectory dataset. Although these methods can capture the particular characteristics of trajectories, they still suffer from the following two disadvantages. (1) These methods cannot capture the sequential information of the trajectory well. (2) These methods only concentrate on the given source and destination which may lead to data sparsity issues. To overcome those shortcomings, we propose a method called {\bf A}nomalous {\bf T}rajectory {\bf D}etection using {\bf R}ecurrent {\bf N}eural {\bf N}etwork (\textbf{ATD-RNN}) which characterizes the trajectory by learning the trajectory embedding. The trajectory embedding can capture the sequential information of the trajectory and depict the internal characteristics between anomalous and norm trajectory. To address the potential data sparsity problem, we enlarge the dataset between a source and a destination by taking the relevant trajectories into consideration. Extend experiments on real-world datasets validate the effectiveness of our method.

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“…It is capable of measuring spatial-temporal distance between sub-trajectories jointly. Another algorithm TPRO [13] divides dataset into groups to obtain the top-k most popular routes. This method labels an outlier if it has a great difference with the selected routes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Detecting Spatial-Temporal Trajectory Outlier

Lv¹,

Zhang²,

Ji³

et al. 2016

Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

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Abstract-As an important area of spatial-temporal data mining, trajectory outlier detection has already attracted broad attention in recent years. In this paper, we present a novel distance measurement between spatial-temporal sub-trajectories and propose algorithm STOD for detecting outliers in both spatial and temporal dimensions jointly. Each trajectory is divided into line segments at first, and the corresponding minimal boundary boxes are constructed. After combination, the outlier index is computed with our distance measurements including overlapping volume, angle and speed. To improve the efficiency of algorithm STOD, we present algorithm PSTOD for parallel detecting spatial-temporal trajectory outlier, which is implemented using Spark framework. The experiment results on real taxi dataset show that the two algorithms are effective and efficient.

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Time-Dependent Popular Routes Based Trajectory Outlier Detection

Cited by 40 publications

References 19 publications

Abnormal Trajectory Detection Based on a Sparse Subgraph

Abnormal Trajectory Detection Based on a Sparse Subgraph

Anomalous Trajectory Detection using Recurrent Neural Network

A Novel Algorithm for Detecting Spatial-Temporal Trajectory Outlier

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