GRASP-UTS: an algorithm for unsupervised trajectory segmentation

Soares, Amílcar; Moreno, Bruno Neiva; Times, Valéria Cesário; Matwin, Stan; Cabral, Lucídio dos Anjos Formiga

doi:10.1080/13658816.2014.938078

Cited by 42 publications

(40 citation statements)

References 18 publications

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“…A cost-function-based approach partitions a trajectory into segments by minimizing a cost function comprised of the conciseness of segments and intra-segment similarity that formalizes the principle of minimum description length (MDL). 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).…”

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

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“…When the segmentation criteria are unknown, the methods are called "unsupervised". Unsupervised algorithms derive the homogeneity of segments based on some cost function, which can avoid any control from the user, but may lack semantics (W-K means) [33] or are time-consuming (GRASP-UTS) [34]. To combine the benefits of both supervised and unsupervised strategies, Junior [35] first proposed a semi-supervised approach RGRASP-SemTS (Reactive Greedy Randomized Adaptive Search Procedure for Semantic semi-supervised Trajectory Segmentation), which exploited labeled and unlabeled data to achieve homogeneity of segments using a cost function based on the minimum description length (MDL) principle.…”

Section: Introductionmentioning

confidence: 99%

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

Cheng

et al. 2019

IJGI

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Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.

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“…Transportation mode estimation involves two steps [11]: (i) extraction of segments of the same transportation modes; and (ii) classification of transportation modes for each segment. For the first step, several segmentation algorithms have been proposed in the past years and include temporal-based [8], cost function-based [5] and semantic-based methods [7]. For the second step, which is the focus of this work, the classification (or prediction) of the transportation modes is performed by creating domain expert features for supervised classification (e.g., the distance between consecutive points, velocities, acceleration, and bearing).…”

Section: Introductionmentioning

confidence: 99%

Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal

Etemad

Soares

Matwin

2018

Lecture Notes in Computer Science

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Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.

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GRASP-UTS: an algorithm for unsupervised trajectory segmentation

Cited by 42 publications

References 18 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

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal

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