UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Guo, Ning; Shekhar, Shashi; Xiong, Wei; Chen, Luo; Jing, Ning

doi:10.3390/ijgi8110518

Cited by 8 publications

(11 citation statements)

References 36 publications

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“…Similarly, the uncertain trajectory similarity measure (UTMS) method uses a modified elliptical model called the Amended Ellipse model. In addition to sampling error, UTMS considers positioning error, but it relies on a constant value for positional error (Guo et al, 2019). In another attempt, Sharif et al (2019) proposed a hierarchal fuzzy inference system, named CaFIRST, to measure the similarity between trajectories enriched by contextual information.…”

Section: Uncertainty Management In Trajectory Similarity Measuresmentioning

confidence: 99%

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FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

Boroumand

Alesheikh

Sharif

et al. 2022

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The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases.

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Section: Uncertainty Management In Trajectory Similarity Measuresmentioning

confidence: 99%

“…Therefore, the way the object moves between the corresponding sampling points is not acquired. To represent a trajectory, the gap between sampling points is filled by linear interpolation (Guo et al, 2019). Accordingly, lower sampling rate results in larger gap areas.…”

Section: Introductionmentioning

confidence: 99%

FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

Boroumand

Alesheikh

Sharif

et al. 2022

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“…The UMS method considers the interpolation error uncertainty of movement, and it was found to be more accurate and robust than related procedures, including EDR, LCSS, and DTW. In another paper, an uncertain trajectory similarity measure was proposed based on an amended ellipse model that could consider the uncertainty of the trajectories caused by noise and discontinuous recording (Guo, Shekhar, Xiong, Chen, & Jing, 2019).…”

Section: Related Workmentioning

confidence: 99%

Trajectory similarity measurement: An enhanced maximal travel match method

Karami

Malek

2021

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Trajectory similarity measurement is a vital and widely used step in many applications, including recommendation systems. In the discipline of similarity measurement, research has mostly been focused on raw trajectories, consisting of location and time‐stamp information. Due to the explosion in the use of the internet and location‐based social networks, raw trajectories can be easily enriched with semantic information. Nevertheless, few attempts have been made to apply semantic and location information during similarity measurement. In light of this, we present a new similarity measure called the enhanced maximal travel match (EMTM) in this article. The proposed EMTM improves on conventional maximal travel match (MTM) methods by simultaneously considering the location and place category as the most basic semantic information. EMTM first generates a new place category‐location hierarchical framework (CLHF) for each trajectory. Subsequently, it identifies MTMs at each layer of hierarchy to explore the similarity between each pair of CLHFs. Finally, the proposed method calculates similarity scores based on the identified MTMs. In experiments on a Foursquare data set, EMTM outperformed the conventional MTM methods by more than 50% in terms of mean average precision. Additionally, EMTM was the most accurate technique among four state‐of‐the‐art methods.

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“…For example, Salarpour and Khotanlou [25] used spectral 3 Wireless Communications and Mobile Computing clustering to segment the trajectory, proposed a trajectory description method based on the change of the subtrajectory direction, and measured the similarity of the described trajectory based on the time warping matching algorithm. Taking into account the uncertainty of trajectory data, Guo et al [26] proposed a similarity measurement method based on an amended ellipse model, referred to as UTSM, to reduce the interpolation error and positioning error. This method has good robustness and tolerance to abnormal data and noise.…”

Section: Related Workmentioning

confidence: 99%

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

Wang

Niu

et al. 2021

Wireless Communications and Mobile Computing

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In the data mining of road networks, trajectory clustering of moving objects plays an important role in many applications. Most existing algorithms for this problem are based on every position point in a trajectory and face a significant challenge in dealing with complex and length-varying trajectories. This paper proposes a grid-based whole trajectory clustering model (GBWTC) in road networks, which regards the trajectory as a whole. In this model, we first propose a trajectory mapping algorithm based on grid estimation, which transforms the trajectories in road network space into grid sequences in grid space and forms grid trajectories by recognizing and eliminating redundant, abnormal, and stranded information of grid sequences. We then design an algorithm to extract initial clustering centers based on density weight and improve a shape similarity measuring algorithm to measure the distance between two grid trajectories. Finally, we dynamically allocate every grid trajectory to the best clusters by the nearest neighbor principle and an outlier function. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Silhouette Coefficient to maximize intercluster separation and intracluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated on a real-world dataset in comparison with existing algorithms.

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UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Cited by 8 publications

References 36 publications

FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

FLCSS: A fuzzy‐based longest common subsequence method for uncertainty management in trajectory similarity measures

Trajectory similarity measurement: An enhanced maximal travel match method

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

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