Clustering speed in multi-lane traffic networks

Bing Zhang; Goce Trajcevski; Feiying Liu

doi:10.1145/2983323.2983905

Clustering speed in multi-lane traffic networks

Bing Zhang, Goce Trajcevski, Feiying Liu

Master of Science in Machine Learning and Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

We address the problem of efficient spatio-temporal clustering of speed data in road segments with multiple lanes. We postulate that the navigation/route plans typically reported by different providers as a single-value need not be accurate in multi-lane networks. Our methodology generates lane-aware distribution of speed from GPS data and agglomerates the basic space and time units into larger clusters. Thus, we achieve a compact description of speed variations which can be subsequently used for more accurate trips planning. We provide experiments that demonstrate the benefits of our proposed approaches.

Original language	English (US)
Title of host publication	CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	2045-2048
Number of pages	4
ISBN (Electronic)	9781450340731
DOIs	https://doi.org/10.1145/2983323.2983905
State	Published - Oct 24 2016
Event	25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: Oct 24 2016 → Oct 28 2016

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
Volume	24-28-October-2016

Other

Other	25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/Territory	United States
City	Indianapolis
Period	10/24/16 → 10/28/16

Funding

Research Supported by the NSF III 1213038 grant Research Supported by the NSF grants CNS 0910952 and III 1213038, and ONR grant N00014-14-10215

Keywords

Multi-lane roads
Speed clustering
Trajectories

ASJC Scopus subject areas

General Business, Management and Accounting
General Decision Sciences

Access to Document

10.1145/2983323.2983905

Cite this

Zhang, B., Trajcevski, G., & Liu, F. (2016). Clustering speed in multi-lane traffic networks. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (pp. 2045-2048). (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983905

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abstract = "We address the problem of efficient spatio-temporal clustering of speed data in road segments with multiple lanes. We postulate that the navigation/route plans typically reported by different providers as a single-value need not be accurate in multi-lane networks. Our methodology generates lane-aware distribution of speed from GPS data and agglomerates the basic space and time units into larger clusters. Thus, we achieve a compact description of speed variations which can be subsequently used for more accurate trips planning. We provide experiments that demonstrate the benefits of our proposed approaches.",

keywords = "Multi-lane roads, Speed clustering, Trajectories",

author = "Bing Zhang and Goce Trajcevski and Feiying Liu",

note = "Funding Information: Research Supported by the NSF III 1213038 grant Research Supported by the NSF grants CNS 0910952 and III 1213038, and ONR grant N00014-14-10215 Publisher Copyright: {\textcopyright} 2016 ACM. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 ; Conference date: 24-10-2016 Through 28-10-2016",

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Zhang, B, Trajcevski, G & Liu, F 2016, Clustering speed in multi-lane traffic networks. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, vol. 24-28-October-2016, Association for Computing Machinery, pp. 2045-2048, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 10/24/16. https://doi.org/10.1145/2983323.2983905

Clustering speed in multi-lane traffic networks. / Zhang, Bing; Trajcevski, Goce; Liu, Feiying.
CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, 2016. p. 2045-2048 (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: Research Supported by the NSF III 1213038 grant Research Supported by the NSF grants CNS 0910952 and III 1213038, and ONR grant N00014-14-10215 Publisher Copyright: © 2016 ACM. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.

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AB - We address the problem of efficient spatio-temporal clustering of speed data in road segments with multiple lanes. We postulate that the navigation/route plans typically reported by different providers as a single-value need not be accurate in multi-lane networks. Our methodology generates lane-aware distribution of speed from GPS data and agglomerates the basic space and time units into larger clusters. Thus, we achieve a compact description of speed variations which can be subsequently used for more accurate trips planning. We provide experiments that demonstrate the benefits of our proposed approaches.

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Clustering speed in multi-lane traffic networks

Abstract

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Funding

Keywords

ASJC Scopus subject areas

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