Density-based trajectory outlier detection algorithm

Liu, Zhipeng; Pi, Dechang; Jiang, Jinfeng

doi:10.1109/jsee.2013.00042

Cited by 45 publications

(22 citation statements)

References 17 publications

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“…To overcome the shortcomings of the distance based methods, Liu et al [23] proposed a density-based trajectory outlier detection algorithm (DBTOD). The DBTOD employ considers the concept of the trajectory density of the neighborhood object distribution.…”

Section: B Density Based Trajectory Detection Methodsmentioning

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: B Density Based Trajectory Detection Methodsmentioning

confidence: 99%

Abnormal Trajectory Detection Based on a Sparse Subgraph

et al. 2020

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“…Typical distance calculation methods include Euclide distance, DTW, LCSS, and Hausdorff distance. Liu et al [22] provided a distance function to calculate the degree of mismatch between two basic comparison units. They definded the concepts of matching and anomaly and then used R-Tree to find the mismatched unit, which will be judged as an anomaly.…”

Section: B Abnormal Trajectory Detectionmentioning

confidence: 99%

A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection

Wang

et al. 2020

IEEE Access

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Nowadays, massive data has been brought by the rapid development of technology. When finding whether trajectory to be detected is abnormal under the premise of given normal trajectories, we innovatively propose 1) Seq2Seq model based on LSTM prediction network for trajectory modelling (SL-Modelling), and 2) abnormal trajectory detection method with spatio-temporal and semantic information. Firstly, SL-Modelling is used to obtain sequence-type trajectory models of normal trajectory groups directly for subsequent detection with no need to extract a large number of features manually and adapting to different sequence length. Then we introduce the concept of distance and semantic interest sequence that makes full use of spatio-temporal and semantic information of trajectories. Finally, the similarity between models and trajectory to be detected is calculated to detect abnormal trajectory. The experimental results of publicly available flight data set show that trajectory models obtained are descriptive enough to represent normal trajectory groups well, and the accuracy of modelling is higher than the existing advanced methods. Besides, the detection with spatio-temporal and semantic information has been verified that it has stronger detection ability with higher accuracy and takes less time.

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“…The distance-based method divides the whole trajectory into sub-trajectories that include certain trajectory points and calculates the Hausdorff distance between sub-trajectories. This method involves high computational intensity and difficulty in addressing GNSS trajectories with high sparsity [22]. The clustering-based method calculates the similarity of trajectories and selects an appropriate clustering method.…”

Section: Purpose and Significancementioning

confidence: 99%

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

2018

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In road networks, an ‘optimal’ trajectory is a geometrically optimal drive from the source point to the destination point. In reality, the driver’s driving experience or road traffic conditions will lead to differences between the ‘actual’ trajectory and the ‘optimal’ trajectory. When the differences are excessive, these trajectories are considered as anomalous trajectories. In addition, these differences can be observed in various trajectory features, such as velocity, distance, turns, and intersections. In this paper, our aim is to fuse these trajectory features and to quantitatively describe this difference to infer anomalous trajectories. The Dempster-Shafer (D-S) evidence theory is a theory and method that uses different features as evidence to infer uncertainty. The theory does not require prior knowledge or conditional probabilities. Therefore, we propose an automatic, anomalous trajectory inference method based on the D-S evidence theory that considers driving behavior and road network constraints. To achieve this objective, we first obtain all of the ‘actual’ trajectories of drivers for different source-destination pairs in taxi Global Navigation Satellite System (GNSS) trajectories. Second, we define and extract five trajectory features: route selection ( R S ), intersection rate ( I R ), heading change rate ( HCR ) , slow point rate ( SPR ), and velocity change rate ( VCR ) . Then, different features of each trajectory are combined as evidence according to Dempster’s combinational rule. The precise probability interval of each trajectory is calculated based on the D-S evidence theory. Finally, we obtain the anomalous possibility of all real trajectories and infer anomalous trajectories whose trajectory features are significantly different from normal ones. The experimental results show that the proposed method can infer anomalous trajectories effectively and that it can be used to monitor driver behavior automatically and to discover adverse urban traffic events.

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Density-based trajectory outlier detection algorithm

Cited by 45 publications

References 17 publications

Abnormal Trajectory Detection Based on a Sparse Subgraph

Abnormal Trajectory Detection Based on a Sparse Subgraph

A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection

Detecting Anomalous Trajectories Using the Dempster-Shafer Evidence Theory Considering Trajectory Features from Taxi GNSS Data

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