Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles o...

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Autores principales:	Chunchun Hu, Si Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	loosely moving congestion patterns parallel computing group patterns equidirectional spatial snapshot cluster Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/28401e3f36bb43b5a88082f43e065361
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id	oai:doaj.org-article:28401e3f36bb43b5a88082f43e065361
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spelling	oai:doaj.org-article:28401e3f36bb43b5a88082f43e0653612021-11-25T17:53:15ZMassively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data10.3390/ijgi101107872220-9964https://doaj.org/article/28401e3f36bb43b5a88082f43e0653612021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/787https://doaj.org/toc/2220-9964The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.Chunchun HuSi ChenMDPI AGarticleloosely moving congestion patternsparallel computinggroup patternsequidirectional spatial snapshot clusterGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 787, p 787 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	loosely moving congestion patterns parallel computing group patterns equidirectional spatial snapshot cluster Geography (General) G1-922
spellingShingle	loosely moving congestion patterns parallel computing group patterns equidirectional spatial snapshot cluster Geography (General) G1-922 Chunchun Hu Si Chen Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
description	The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.
format	article
author	Chunchun Hu Si Chen
author_facet	Chunchun Hu Si Chen
author_sort	Chunchun Hu
title	Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
title_short	Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
title_full	Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
title_fullStr	Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
title_full_unstemmed	Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data
title_sort	massively parallel discovery of loosely moving congestion patterns from trajectory data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/28401e3f36bb43b5a88082f43e065361
work_keys_str_mv	AT chunchunhu massivelyparalleldiscoveryoflooselymovingcongestionpatternsfromtrajectorydata AT sichen massivelyparalleldiscoveryoflooselymovingcongestionpatternsfromtrajectorydata
_version_	1718411853130891264

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

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