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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Daganzo, C.F.: Urban gridlock: macroscopic modeling and mitigation approaches. Transp. Res. Part B: Methodol. 41(1), 49–62 (2007)
Geroliminis, N., Daganzo, C.F.: Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp. Res. Part B: Methodol. 42(9), 759–770 (2008)
Lu, X.-Y., Varaiya, P., Horowitz, R., Skabardonis, A.: Fundamental diagram modeling and analysis based NGSIM data. In: 12th IFAC Symposium on Control in Transportation Systems, Redondo Beach California (2009)
Dervisoglu, D., Gomes, G., Kwon, J., Horowitz, R., Varaiya, P.: Automatic calibration of the fundamental diagram and empirical observations on capacity. In: 88th Annual Meeting of the Transportation Research Board, Washington, D.C. (2009)
Wang, Y., Papageorgiou, M., Messmer, A.: Real-time freeway traffic state estimation based on extended kalman filter: a case study. Transp. Sci. 41(2), 167–181 (2007)
Daganzo, C.F.: The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp. Res. Part B: Methodol. 28(4), 269–287 (1994)
Celikoglu, H.B.: An approach to dynamic classification of traffic flow patterns. Comput. Aided Civ. Infrastruct. Eng. 28(4), 273–288 (2013)
Celikoglu, H.B.: Dynamic classification of traffic flow patterns simulated by a multi-mode discrete cell transmission model. IEEE Trans. Intell. Transp. Syst. 15(6), 2539–2550 (2014)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, College Div. (1988)
Silgu, M.A., Celikoglu, H.B.: K-means clustering method to classify freeway traffic flow patterns. Pamukkale J. Eng. Sci. 20(6), 232–239 (2014)
Highway Capacity Manual 2010, Transportation Research Board of the National Academies (2010)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1974)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Press, New York (1981)
Babuska, R.: Fuzzy clustering algorithms with applications to rule extraction. In: Sczepaniak, P.S., et al. (eds.) Fuzzy Systems in Medicine, pp. 139–173. Springer, Heidelberg (2000)
Pal, N.R., Bezdek, J.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (1995)
Spiegel, M.R., Stephens, L.J.: Schaum’s Outline of Statistics, 4th edn. McGraw Hill, New York (2011)
Silgu, M.A.: Multivariate and fuzzy clustering approaches to dynamic classification of traffic flow states. M.Sc. thesis submitted to Istanbul Technical University Graduate School of Science Engineering and Technology (2015)
Acknowledgments
The authors would like to thank Onur Deniz for contributions in coding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Silgu, M.A., Celikoglu, H.B. (2015). Clustering Traffic Flow Patterns by Fuzzy C-Means Method: Some Preliminary Findings. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_93
Download citation
DOI: https://doi.org/10.1007/978-3-319-27340-2_93
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27339-6
Online ISBN: 978-3-319-27340-2
eBook Packages: Computer ScienceComputer Science (R0)