More Web Proxy on the site http://driver.im/

research-article

GPS trajectory clustering method for decision making on intelligent transportation systems

Authors: Gary Reyes, Laura Lanzarini, Waldo Hasperué, Aurelio F. BarivieraAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 38, Issue 5

Pages 5529 - 5535

https://doi.org/10.3233/JIFS-179644

Published: 01 January 2020 Publication History

Abstract

Technological progress facilitates recording and collecting information on vehicles’ GPS trajectories on public roads. The intelligent analysis of this data leads to the identification of extremely useful patterns when making decisions in situations related to urbanism, traffic and road congestion, among others. This article presents a GPS trajectory clustering method that uses angular information to segment the trajectories and a similarity function guided by a pivot. In order to initialize the process, it is proposed to segment the region to be analyzed in a uniform way forming a grid. The obtained results after applying the proposed method on a real trajectories database are satisfactory and show significant improvement in comparison with the methods published in the bibliography.

References

[1]

Besse P.C., Guillouet B., Loubes J. and Royer F., Review and perspective for distance-based clustering of vehicle trajectories, IEEE Transactions on Intelligent Transportation Systems 17(11) (2016), 3306–3317.

Digital Library

[2]

D’Andrea E., Di Lorenzo D., Lazzerini B., Marcelloni F. and Schoen F., Path clustering based on a novel dissimilarity function for ride-sharing recommenders, In 2016 IEEE International Conference on Smart Computing(SMARTCOMP), 1–8, May 2016.

[3]

Ester M., Kriegel H.-P., Sander J. and Xu X., A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise, In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, 226–231. AAAI Press, 1996.

[4]

Fountoulakis M., Bekiaris-Liberis N., Roncoli C., Papamichail I. and Papageorgiou M., Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing, Transportation Research Part C: Emerging Technologies 78 (2017), 13–33.

[5]

Gao Y. and Leung Maylor K.H., Line segment hausdorff distance on face matching, Pattern Recognition 35(2) (2002), 361–371.

[6]

Guerrero-Ibañez J., Zeadally S. and Contreras-Castillo J., Sensor technologies for intelligent transportation systems, Sensors 18(4) (2018).

[7]

Hausdorff F., Bemerkung über den inhalt von Punktmengen, Mathematische Annalen 75 (1914), 428–433.

[8]

Jiang X., de Souza E.N., Pesaranghader A., Hu B., Silver D. and Matwin S., Trajectorynet: An embedded gps trajectory representation for point-based classification using recurrent neural networks, 05, 2017.

[9]

Lee J.-G., Han J. and Whang K.-Y., Trajectory clustering: A partition-and-group framework, In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD ’07, 593–604, New York, NY, USA, 2007. ACM.

[10]

Xu Liu L., Song J.T., Guan B., Xiao Wu Z. and He K.J., Tra-DBScan: A algorithm of clustering trajectories, In Frontiers of Manufacturing and Design Science II, volume 121 of Applied Mechanics and Materials, 4875–4879. Trans Tech Publications Ltd, 1 2012.

[11]

Mao Y., Zhong H., Qi H., Ping P. and Li X., An adaptive trajectory clustering method based on grid and density in mobile pattern analysis, Sensors 17(9) (2017).

[12]

Marković N., Sekuła P., Vander Laan Z., Andrienko G. and Andrienko N., Applications of trajectory data from the perspective of a road transportation agency: Literature review and Maryland case study, IEEE Transactions on Intelligent Transportation Systems 20(5) (2019), 1858–1869.

[13]

Menouar H., Guvenc I., Akkaya K., Uluagac A.S., Kadri A. and Tuncer Adem, Uav-enabled intelligent transportation systems for the smart city: Applications and challenges, Comm Mag 55(3) (2017), 22–28.

Digital Library

[14]

Pakhira M.K., Bandyopadhyay S. and Maulik U., Validity index for crisp and fuzzy clusters, Pattern Recognition 37(3) (2004), 487–501.

[15]

Shen Y., Zhao L. and Fan J., Analysis and visualization for hot spot based route recommendation using short-dated taxi gps traces, Information 6(2) (2015), 134–151.

[16]

Treves F., Topological Vector Spaces, Distributions and Kernels, Dover books on mathematics, Dover Publications, 2006.

[17]

Wang Y., Qin K., Chen Y. and Zhao P., Detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi gps data, ISPRS Int J Geo-Information 7(25) (2018).

[18]

Zhou X., Miao F., Ma H., Zhang H. and Gong H., A trajectory regression clustering technique combining a novel fuzzy c-means clustering algorithm with the least squares method, ISPRS International Journal of Geo-Information 7(5) (2018).

Cited By

Ma DFang BMa WWu XJin S(2023)Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory ClusteringIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.328803024:11(11878-11888)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3288030
Kumar BHariharan KShanmugam RShriram SSridhar J(2022)Enabling internet of things in road traffic forecasting with deep learning modelsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22023043:5(6265-6276)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-220230

Index Terms

GPS trajectory clustering method for decision making on intelligent transportation systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Trajectory clustering: a partition-and-group framework
SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories ...
Highway Accident Localization Based on Virtual Fence for Intelligent Transportation Systems
Web Information Systems and Applications
Abstract
It is important to determine the mile marker information of traffic accident (i.e., accident localization) for highway repairs and emergency rescue services in intelligent transportation systems. Since traditional localization methods usually ...
Timing and security analysis of VANET-based intelligent transportation systems
ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

With the fast development of autonomous driving and vehicular communication technologies, intelligent transportation systems that are based on VANET (Vehicular Ad-Hoc Network) have shown great promise. For instance, through V2V (Vehicle-to-Vehicle) and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 38, Issue 5

Special section: Intelligent data analysis and applications & smart vehicular technology, communications and applications

2020

1353 pages

ISSN:1064-1246

Issue’s Table of Contents

© 2020 – IOS Press and the authors. All rights reserved.

Publisher

IOS Press

Netherlands

Publication History

Published: 01 January 2020

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ma DFang BMa WWu XJin S(2023)Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory ClusteringIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.328803024:11(11878-11888)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3288030
Kumar BHariharan KShanmugam RShriram SSridhar J(2022)Enabling internet of things in road traffic forecasting with deep learning modelsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22023043:5(6265-6276)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-220230

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents