Scalable Distributed Subtrajectory Clustering

Tampakis, Panagiotis; Pelekis, Nikos; Doulkeridis, Christos; Theodoridis, Yannis

doi:10.1109/bigdata47090.2019.9005563

Cited by 23 publications

(13 citation statements)

References 26 publications

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“…Most of these methods consider only spatial data; that is, they disregard spatiotemporal data [4]. TraClus [12] is a famous classical method in trajectory clustering [1][2] [18] and processes spatiotemporal data well. TraClus was specifically designed for trajectory clustering and widely used for the purpose.…”

Section: Non-deep Learning Methodsmentioning

confidence: 99%

HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering

Hsu¹

2021

Preprint

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The Estimated Time of Arrival (ETA) that predict the travel time of a given GPS trajectory has been extensively used in route planning. Deep learning has been widely applied to ETA prediction. However, prediction tasks involve some challenges, such as small data size, low GPU’s precision, high training loss, and low accuracy. Herein, we present a new machine-learning algorithm called HyperETA for ETA prediction. HyperETA is based on a extraordinary clustering method, called Hypercube Clustering. We conducted experiments to compare HyperETA with a deep-learning-based method called DeepTTE by using taxi trajectories as a benchmark. Two variations of both methods were evaluated. The results indicated that HyperETA outperformed the deep-learning approach in terms of prediction accuracy.

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Section: Non-deep Learning Methodsmentioning

confidence: 99%

HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering

Hsu¹

2021

Preprint

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“…One data clustering method designed specifically for trajectory clustering is Tra-Clus [24]. TraClus is a famous classical method in trajectory clustering [1], [3], [28], which is evaluated in this study.…”

Section: B Methods Of Finding Common Trajectorymentioning

confidence: 99%

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Hsu

Lee

2020

IEEE Access

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Conventional sub-trajectory clustering is used to identify similarities among multiple trajectories. Existing methods tend to overlook many of the relevant sub-trajectories; others require a road network as input; all are significantly slowed down considerably by large datasets. In this paper, we propose a novel approach to clustering sub-trajectory in which trajectories are transformed into a set of Hypercubes. The Hypercubes are pairwise-matched to find an intersection and then clustered accordingly. The performance of the proposed scheme was compared with that of grid clustering (i.e., constant time technique) in terms of memory usage, computational speed, and compared with a state-of-art method, TraClus, by assessing their accuracy. The experiment results show that Hypercube clustering can identify common sub-trajectories more swiftly and with less memory usage than grid clustering. The accuracy of Hypercube clustering is superior to TraClus. INDEX TERMS Urban computing, similar trajectories, ridesharing paths, common sub-trajectories clustering.

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“…For the above reasons, the desired specifications that such a trajectory clustering algorithm should hold, in order to be able to predict the movement of future trajectories, are the following: There have been some approaches to deal with the problem of subtrajectory clustering in a centralized way [1,21,32], however, all the above subtrajectory clustering approaches are centralized and do not scale with the size of today's trajectory data, thus calling for parallel and distributed algorithms. For this reason, we utilize the work presented in [36], coined DSC, which introduces an efficient and highly scalable approach to deal with the problem of Distributed Subtrajectory Clustering, by means of MapReduce. More specifically, the authors of [36] split the original problem to three sub-problems, namely Subtrajectory Join, Trajectory Segmentation and Clustering and Outlier Detection, and deal with each one in a distributed fashion by utilizing the MapReduce programming model.…”

Section: Offline Step: Mobility Pattern Extraction Based On Sub-trajementioning

confidence: 99%

“…For this reason, we utilize the work presented in [36], coined DSC, which introduces an efficient and highly scalable approach to deal with the problem of Distributed Subtrajectory Clustering, by means of MapReduce. More specifically, the authors of [36] split the original problem to three sub-problems, namely Subtrajectory Join, Trajectory Segmentation and Clustering and Outlier Detection, and deal with each one in a distributed fashion by utilizing the MapReduce programming model.…”

Section: Offline Step: Mobility Pattern Extraction Based On Sub-trajementioning

confidence: 99%

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Online Long-Term Trajectory Prediction Based on Mined Route Patterns

Πέτρου

Tampakis

Georgiou

et al. 2020

Lecture Notes in Computer Science

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In this paper, we present a Big data framework for the prediction of streaming trajectory data by exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. In particular, to meet this goal we follow a two-step methodology. First, we efficiently identify the hidden mobility patterns in an offline manner. Subsequently, the trajectory prediction algorithm exploits these patterns in order to prolong the temporal horizon of useful predictions. The experimental study is based on real-world aviation and maritime datasets.

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Scalable Distributed Subtrajectory Clustering

Cited by 23 publications

References 26 publications

HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering

HyperETA: An Estimated Time of Arrival Method based on Hypercube Clustering

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Online Long-Term Trajectory Prediction Based on Mined Route Patterns

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