ONF-TRS: On-line Noise Filtering Algorithm for Trajectory Segmentation Based on MDL Threshold

Riyadh, Musaab; Mustapha, Norwati; Sulaiman, Nasir; Sharef, Nurfadlina Binti Moh

doi:10.3923/jai.2017.42.48

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The cost function was defined in terms of the spatial distance for line segments (Lee et al 2007), and the attribute distance between representative points and other points within segments (Júnior et al 2015). The similar approach was proposed for noise filtering (Riyadh et al 2017). This approach is useful in identifying characteristic points (e.g.…”

Section: Related Workmentioning

confidence: 99%

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

Hwang

VanDeMark

Dhatt

et al. 2018

International Journal of Geographical Information Science

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This paper considers the problem of partitioning an individual GPS trajectory data into homogeneous, meaningful segments such as stops and trips. Signal loss and signal noise are highly prevalent in human trajectory data, and it is challenging to deal with uncertainties in segmentation algorithms. We propose a new trajectory segmentation algorithm that detects stop segments in a noiserobust manner from GPS data with time gaps. The algorithm consists of three steps that impute time gaps, split data into base segments and estimate states over a base segment. The statedependent path interpolation was proposed as a framework for gap imputation to deal with locational and temporal uncertainties associated with signal loss. A spatiotemporal clustering-based trajectory segmentation was proposed to detect spatiotemporal clusters of any shape regardless of density to cut a trajectory into internally similar base segments. Fuzzy inference was employed to deal with borderline cases in determining states over base segments based on input features. The proposed algorithm was applied to detect stop/move episodes from raw GPS trajectories that were collected from 20 urban and 19 suburban participants. Sensitivity analysis was conducted to guide the choice of parameters such as the temporal and spatial definitions of a stop. Experimentation results show that the proposed method correctly identified 92% of stop/move episodes, and correctly estimated 98% of episode duration. This study indicates that a sequence of statedependent gap imputation, clustering-based data segmentation and fuzzy-set-based state estimation can satisfactorily deal with uncertainty in processing human GPS trajectory data.

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Section: Related Workmentioning

confidence: 99%

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

Hwang

VanDeMark

Dhatt

et al. 2018

International Journal of Geographical Information Science

View full text Add to dashboard Cite

show abstract

“…Two real data sets called Elk1993 and Deer1995 are used; the Elk 93 has 33 trajectories and 47204 points, while Deer 95 has 32 trajectories and 20065 points. Any trajectory segmentation algorithm such as in [10,21] can be used to divide each trajectory to set of segments. Matlab R2012b and excel 2013 were used to implement the algorithm and plot the charts.…”

Section: Performance Evaluationmentioning

confidence: 99%

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

Riyadh

Mustapha

Sulaiman

et al. 2017

Mathematical Problems in Engineering

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The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

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AIS data driven general vessel destination prediction: A random forest based approach

Zhang

Bin

Wang

et al. 2020

Transportation Research Part C: Emerging Technologies

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ONF-TRS: On-line Noise Filtering Algorithm for Trajectory Segmentation Based on MDL Threshold

Cited by 4 publications

References 7 publications

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

Segmenting human trajectory data by movement states while addressing signal loss and signal noise

CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

AIS data driven general vessel destination prediction: A random forest based approach

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