Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings

Mehmet Ali Silgu; Hilmi Berk Celikoglu

doi:10.1007/978-3-319-27340-2_93

Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings

Mehmet Ali Silgu^*, Hilmi Berk Celikoglu

^*Corresponding author for this work

Department of Civil Engineering

Istanbul Technical University

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

24 Citations (Scopus)

Abstract

In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
Editors	Franz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
Publisher	Springer Verlag
Pages	756-764
Number of pages	9
ISBN (Print)	9783319273396
DOIs	https://doi.org/10.1007/978-3-319-27340-2_93
Publication status	Published - 2015
Event	15th International Conference on Computer Aided Systems Theory, EUROCAST 2015 - Las Palmas de Gran Canaria, Spain Duration: 8 Feb 2015 → 13 Feb 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9520
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computer Aided Systems Theory, EUROCAST 2015
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	8/02/15 → 13/02/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Clustering
Flow pattern
Fuzzy c-means
Vehicular traffic flow

Access to Document

10.1007/978-3-319-27340-2_93

Cite this

Silgu, M. A., & Celikoglu, H. B. (2015). Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings. In F. Pichler, R. Moreno-Díaz, & A. Quesada-Arencibia (Eds.), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers (pp. 756-764). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520). Springer Verlag. https://doi.org/10.1007/978-3-319-27340-2_93

Silgu, Mehmet Ali ; Celikoglu, Hilmi Berk. / Clustering traffic flow patterns by fuzzy c-means method : Some preliminary findings. Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. editor / Franz Pichler ; Roberto Moreno-Díaz ; Alexis Quesada-Arencibia. Springer Verlag, 2015. pp. 756-764 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.",

keywords = "Clustering, Flow pattern, Fuzzy c-means, Vehicular traffic flow",

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Silgu, MA & Celikoglu, HB 2015, Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings. in F Pichler, R Moreno-Díaz & A Quesada-Arencibia (eds), Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9520, Springer Verlag, pp. 756-764, 15th International Conference on Computer Aided Systems Theory, EUROCAST 2015, Las Palmas de Gran Canaria, Spain, 8/02/15. https://doi.org/10.1007/978-3-319-27340-2_93

Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings. / Silgu, Mehmet Ali; Celikoglu, Hilmi Berk.
Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. ed. / Franz Pichler; Roberto Moreno-Díaz; Alexis Quesada-Arencibia. Springer Verlag, 2015. p. 756-764 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9520).

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

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PY - 2015

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N2 - In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.

AB - In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.

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Silgu MA, Celikoglu HB. Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings. In Pichler F, Moreno-Díaz R, Quesada-Arencibia A, editors, Computer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers. Springer Verlag. 2015. p. 756-764. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27340-2_93

Clustering traffic flow patterns by fuzzy c-means method: Some preliminary findings

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Keywords

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