Abstract
Considering the complexity of spatial and temporal changes of traffic data and the huge nature of traffic data, in order to find a sensitive analysis method for traffic congestion state changes, this paper conducts research on cluster analysis model. Firstly, the clustering analysis model is divided into four parts for discussion: clustering analysis algorithm, clustering analysis index selection, clustering number determination and index weight discussion. Then, the different methods contained in each part are recombined to obtain different cluster analysis models. Finally, three clustering analysis models are summarized: Model I, Model II and Model III. In the next stage, evaluation indicators will be selected for quantitative evaluation of the three models to verify the stability of the model method.
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References
Shao, C.F., Weili, Y., Jia, B.: Traffic Flow Theory, 1st edn. Electronic Industry Press, Bei**g (2012)
Chen, F., Jia, Y., Niu, Z.: Study on traffic state partition method based on fuzzy c-means clustering. In: Proceedings of 2010 Asia-Pacific Conference on Information Theory (APCIT 2010) (2010)
Zhang, L., Jia Y., Niu, Z., Liao, C.: Traffic state classification based on parameter weighting and clustering method. J. Transp. Syst. Eng. Inf. Technol. (06),147–151 (2014)
Kaneko, Y., Ohe, I., Kawashima, H., Hirano, T.: The judgement of the traffic condition by using the cluster analysis. In: Presented at Pacific Rim TransTech Conference. Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future, Seattle, (2004)
Diamantopoulos, T., Kehagias, D., König, F., Tzovaras, D.: Use of density-based cluster analysis and categoryification techniques for traffic congestion prediction and visualisation. In: Presented at Transport Research Arena 2014, Paris, (2014)
Anbaroglu, B., Heydecker, B., Cheng, T.: Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks. Transp. Res. Part C 48(11), 47–65 (2014)
Yang, Z., Huang, X., Du, C., Tang, M.: Study of urban traffic congestion judgment based on FFCM clustering. Appl. Res. Comput. 25(9), 2768–2770 (2008)
Fengbo, Z.H.A.O., Min, S.U.N.: Traffic state recognition method based on fuzzy cluster analysis. Microcomput. Appl. 22(02), 9–13 (2006)
Li, T., Liu, J.: Cluster-based spatiotemporal background traffic generation for network simulation, Miami, United States. ACM Trans. Model. Comput. Simul. 25(4), 1–25 (2014)
Wang, Y., Lu H., Qin, X.: Urban road network comprehensive evaluation based on gray cluster analysis. J. Highway Transp. Res. Dev. 22(08), 118–121 (2005)
Li, M., Yu, L., Song, G., **e, L.: Traffic state clustering approach to lower the estimation uncertainty of greenhouse gas emissions based on traffic spatial and temporal characteristics. In: Transportation Research Board Annual Meeting (2016)
Wen, H., Sun, J., Zhang, X.: Study on traffic congestion modes of large city in china taking Bei**g as an example. In: The 9th International Conference on Traffic & Transportation Studies (ICTTS’2014), Shaoxing (2014)
Weiqun, Z.: Research on construction of index system and method of optimal benign evaluation. Stat. Inf. Forum. 21(06), 36–39 (2006)
Acknowledgments
The project is supported by Scientific research project of State Nuclear Electric Power Planning Design & Research Institute Co., Ltd (Number 100-KY2019-HYK-Z01).
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Su, K., Sun, Y. (2023). Study on Clustering Analysis Model of Traffic Congestion State. In: Zeng, X., **e, X., Sun, J., Ma, L., Chen, Y. (eds) Proceedings of the 5th International Symposium for Intelligent Transportation and Smart City (ITASC). ITASC 2022. Lecture Notes in Electrical Engineering, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-99-2252-9_17
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DOI: https://doi.org/10.1007/978-981-99-2252-9_17
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