Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Jiang, Qiaowen; Liu, Yu; Ding, Ziran; Sun, Shun

doi:10.1016/j.cja.2022.10.010

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…We compare the DTID-STFC method with four trajectory clustering models: ST-DBSCAN [21], MTCA [16], MIF-STKNNDC [42], ISCM [30], and HDBSCAN-extended [22].…”

Section: Experiments In Simulated Trajectorymentioning

confidence: 99%

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

Du,

Chen,

et al. 2024

Remote Sensing

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Oceanic trajectories frequently exhibit multiple periodic patterns across various time intervals, e.g., tidal variations, mesoscale eddies, and El Niño events correspond to diurnal, seasonal, and interannual fluctuations in environmental factors. To explore hidden spatiotemporal multiple periodic behaviors in noisy ocean data, we propose a novel trajectory clustering method, namely DTID-STFC. It first identifies dense time intervals (DTIs) in which trajectories occur frequently. Subsequently, within each DTI, it utilizes spectral embedding to project trajectories onto a latent subspace and proposes three-way fuzzy clustering to obtain results. We evaluate the proposed method on simulated datasets and compare it with traditional and state-of-the-art trajectory clustering approaches. Experimental results indicate that it outperforms other methods across all five metrics. Moreover, when applying the DTID-STFC method to the analysis of mesoscale cyclonic eddies in the South China Sea and vessel data, it demonstrates more discernible results than traditional methods, and it aligns well with physical oceanographic processes. This proposed method offers valuable insights into identifying periodic behaviors from complex and noisy spatiotemporal oceanic trajectory data.

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“…We compare the DTID-STFC method with four trajectory clustering models: ST-DBSCAN [21], MTCA [16], MIF-STKNNDC [42], ISCM [30], and HDBSCAN-extended [22].…”

Section: Experiments In Simulated Trajectorymentioning

confidence: 99%

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

Du,

Chen,

et al. 2024

Remote Sensing

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“…In recent years, some classic clustering algorithms have been developed and expanded, such as modified k-means clustering algorithm [13], density spatial clustering algorithm [14], K-nearest neighbor decision clustering algorithm [15], density deviation multi-peaks automatic clustering algorithm [16], and synchronization-inspired clustering algorithm [17]. The classic clustering algorithms are effective when the data structures are the combination of simple form and the features are representative.…”

Section: Classic Clusteringmentioning

confidence: 99%

“…Clustering analysis has grown in popularity, given the need for detecting abnormal flight characteristics. Jiang et al [15] used the density-based spatial clustering algorithm to identify abnormal spatiotemporal flight trajectories on the basis of the pilot's operation. To address the negative impacts of outliers during clustering, Liu et al [18] proposed a clustering with the outlier removal algorithm and used it to detect abnormal flight trajectories.…”

Section: Classic Clusteringmentioning

confidence: 99%

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Flight risk evaluation based on flight state deep clustering network

Wang

Pei

et al. 2023

Complex Intell. Syst.

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Flight risk evaluation based on data-driven approach is an essential topic of aviation safety management. Existing risk analysis methods ignore the coupling and time-variant characteristics of flight parameters, and cannot accurately establish the mapping relationship between flight state and loss-of-control risk. To deal with the problem, a flight state deep clustering network (FSDCN) model was proposed to mine latent loss-of-control risk information implicating in raw flight parameters. FSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network to extract latent risk features from multivariate time-series flight parameters and cluster them. In the FSDCN model, a sequential multi-attention encoder–decoder network is designed to extract embedded risk features, and the feature clustering layer is designed to iteratively refine clustering effects and feature extraction. Besides, a loss-of-control classifier is added to optimize the risk feature vector expression and ensure sufficient dividing feature for facilitate clustering. The multi-task joint learning strategy is adopted to improve the clustering performance of the model further. According to extracted risk features and similarity metrics, the optimal clusters number of flight states is set as 5. Comparative experiments show that FSDCN significantly performs better than other clustering models with performance percentage error below $$6\%$$ 6 % . Through statistical analysis of clustering results, the risk level is quantified for each cluster. Three high-difficulty maneuver cases are presented to demonstrate FSDCN for flight risk evaluation. The flight parameter sequences of the maneuver cases are input into the well-trained FSDCN to obtain the risk prediction results. The spatiotemporal distribution characteristics of the risk-quantized results are consistent with flight parameters over-limit situations, which demonstrates the effectiveness of FSDCN on clustering flight states. The experimental results on flight maneuver cases show that FSDCN can find potential loss-of-control risk features according to multivariate time-series flight data and provide support for in-flight risk warnings.

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“…In 2022, Ahmed et al [10] proposed a graph-based method for detecting outliers in the trajectory. In 2023, Jiang et al [11], in order to mine frequent behaviors of targets from complex historical trajectory data, proposed a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion. Lan et al [12] proposed a two-stage framework for indoor human trajectory anomaly detection based on density noisy application spatial clustering (DBSCAN), which is used to detect human trajectory anomalies in indoor spaces.…”

Section: Introductionmentioning

confidence: 99%

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

Xie,

Guo,

Zheng

2024

Mathematics

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As a type of time series data, trajectory data objectively record the location information and corresponding time information of an object’s activities. It not only describes the spatial activity trajectory of a moving object but also contains the unique attributes, states, and behavioral characteristics of the moving object itself. It can also reflect the interaction relationship between the object’s activities and various elements in the environment to a certain extent. Therefore, mining from moving target trajectory data to discover implicit, effective, and potentially useful spatiotemporal behavior patterns of moving targets, such as anomaly detection, will have significant research significance. This paper proposes a method for mining abnormal patterns in the trajectory of moving targets based on multi-attribute classification. Firstly, to explore the activity location patterns of single moving targets, a frequent sequence discovery method for moving targets based on sequence patterns is proposed. Furthermore, for moving target trajectory data sets containing multiple attributes, numerical attributes are extracted, and the data are clustered according to attribute classification to extract a set of normal behavior patterns of moving targets. Then, combining the activity location patterns and normal behavior patterns of the moving target, the original trajectory data are compared with them to achieve the goal of detecting abnormal behavior of the moving target. Finally, an incremental anomaly detection scheme is proposed to address the characteristics of fast updates and large numbers of data in trajectory data sets. This involves synchronously updating the frequency of moving target activity patterns and the range of values for normal behavior patterns while updating the trajectory data set, in order to meet the needs of database updates and improve the accuracy and credibility of results.

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Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Cited by 7 publications

References 34 publications

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic Trajectories

Flight risk evaluation based on flight state deep clustering network

Mining Abnormal Patterns in Moving Target Trajectories Based on Multi-Attribute Classification

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