Effective and efficient trajectory outlier detection based on time-dependent popular route

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

doi:10.1007/s11280-016-0400-6

Cited by 64 publications

(22 citation statements)

References 24 publications

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“…Mesh generation needs the support of urban road network, remote sensing and other related technologies, which can only be used in some applications. Zhu et al [18] improved TPRO and proposed TPRRO, which introduced geospatial information to realize off-line and on-line real-time anomaly detection. Most of the recent abnormal trajectory detection research focus on classification-and distance-based detection methods.…”

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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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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“…Trajectory data mining has been a hot research topic recently with the availability of massive spatial trajectory data [3,11,31,32,35]. Zheng [32] conducts a systematic survey on this filed, including trajectory data preprocessing, trajectory data management, and a variety of mining tasks.…”

Section: Related Workmentioning

confidence: 99%

MPE: a mobility pattern embedding model for predicting next locations

Chen

Liu

2018

World Wide Web

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The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of "phantom transitions" arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

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“…In recent years, anomalous trajectory detection has included the distance-based method [16,17], the clustering-based method [12,18,19], the classification-based method [20,21], and the grid-based method [13][14][15]. The distance-based method divides the whole trajectory into sub-trajectories that include certain trajectory points and calculates the Hausdorff distance between sub-trajectories.…”

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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Effective and efficient trajectory outlier detection based on time-dependent popular route

Cited by 64 publications

References 24 publications

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

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

MPE: a mobility pattern embedding model for predicting next locations

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

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