On a clustering-based approach for traffic sub-area division

Jiahui Zhu, Xinzheng Niu, Chase Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employs k-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on the SDbw indicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.

Original languageEnglish (US)
Title of host publicationAdvances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings
EditorsFranz Wotawa, Ingo Pill, Roxane Koitz-Hristov, Gerhard Friedrich, Moonis Ali
PublisherSpringer Verlag
Pages516-529
Number of pages14
ISBN (Print)9783030229986
DOIs
StatePublished - Jan 1 2019
Event32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019 - Graz, Austria
Duration: Jul 9 2019Jul 11 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11606 LNAI

Conference

Conference32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019
CountryAustria
CityGraz
Period7/9/197/11/19

Fingerprint

Division
Traffic
Clustering
Trajectories
Traffic Management
Traffic Control
K-means Algorithm
Spatial Information
Clustering Methods
Similarity Measure
Uniformity
Performance Evaluation
Partitioning
Trajectory
Experimental Results
Design

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Clustering
  • Density
  • Hot region
  • Traffic sub-area
  • Vehicle trajectory

Cite this

Zhu, J., Niu, X., & Wu, C. (2019). On a clustering-based approach for traffic sub-area division. In F. Wotawa, I. Pill, R. Koitz-Hristov, G. Friedrich, & M. Ali (Eds.), Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings (pp. 516-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11606 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-22999-3_45
Zhu, Jiahui ; Niu, Xinzheng ; Wu, Chase. / On a clustering-based approach for traffic sub-area division. Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings. editor / Franz Wotawa ; Ingo Pill ; Roxane Koitz-Hristov ; Gerhard Friedrich ; Moonis Ali. Springer Verlag, 2019. pp. 516-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Zhu, J, Niu, X & Wu, C 2019, On a clustering-based approach for traffic sub-area division. in F Wotawa, I Pill, R Koitz-Hristov, G Friedrich & M Ali (eds), Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11606 LNAI, Springer Verlag, pp. 516-529, 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, 7/9/19. https://doi.org/10.1007/978-3-030-22999-3_45

On a clustering-based approach for traffic sub-area division. / Zhu, Jiahui; Niu, Xinzheng; Wu, Chase.

Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings. ed. / Franz Wotawa; Ingo Pill; Roxane Koitz-Hristov; Gerhard Friedrich; Moonis Ali. Springer Verlag, 2019. p. 516-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11606 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employs k-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on the SDbw indicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.

AB - Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employs k-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on the SDbw indicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.

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Zhu J, Niu X, Wu C. On a clustering-based approach for traffic sub-area division. In Wotawa F, Pill I, Koitz-Hristov R, Friedrich G, Ali M, editors, Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings. Springer Verlag. 2019. p. 516-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-22999-3_45