Abstract
Traffic congestion identification is a popular research topic of intelligent transport systems. Identification rate of existing methods usually cannot meet the practical requirements. To improve the identification rate and reduce the computation cost, a novel intelligent identification method is proposed. The proposed method combines principal component analysis (PCA) method with higher-order Boltzmann machine (BM). PCA is used to reduce the dimension of input feature space. It can not only reduce the computation cost but also filter noise of the source data. BM is a kind of stochastic network that can obtain the global optimum solution. Higher-order BM without hidden units can save lots of computation cost without decreasing modeling power. The trained higher-order BM is used to identify traffic state. The efficiency of the proposed method is illustrated through analyzing Jinan urban transportation data.
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Jiang G (2004) Road traffic state detection. China Communications Press
Dudek C, Messer C, Nuckles N (1974) Incident detection on urban freeways. Transportation Research Record, Washington, DC
Persaud B, Hall F, Hall L (1990) Congestion identification aspects of the McMaster detection algorithm. Transp Res Rec 1287:167–175
Tauseef G Rajib C, Rami A (2004) Congestion detection and control in partial mesh using Bayesian approach. In: Student conference on engineering, sciences and technology, pp 152–164
Zhuang B, Yang X, Li K (2006) Criterion and detection algorithm for road traffic congestion incidents. China J Hwy Transp 19:82–86
Jiang G, Gang L, Wang J (2006) Traffic congestion identification method of urban expressway. J Traffic Transp Eng 6:87–91
Chen Y, Zhang Y (2006) Pattern discovering of regional traffic status with self-organizing maps. In: IEEE intelligent transportation systems conference. Toronto, Canada, 17–20 Sep 2006, pp 647–652
Graña M, d’Anjou A, Albizuri F (1997) Experiments of fast learning with high order Boltzmann machines. Appl Intell 7:287–303
Albizuri F, d’Anjou A, Graña M, Torrealdea J, Hernandez M (1995) The high-order Boltzmann machine: learned distribution and topology. IEEE Trans Neural Netw 6:767–770. doi:10.1109/72.377984
Hou Z, Song K, Gupta M, Tan M (2006) Neural units with higher-order synaptic operations for robotic image processing applications. Soft Comput 11:221–228. doi:10.1007/s00500-006-0064-8
Simon H (2001) Neural networks: a comprehensive foundation. Prentice Hall, New York
Hou Z (2005) Principal component analysis (PCA) for data fusion and navigation of mobile robots. In: Kantor P et al (eds) Intelligence and security informatics. Lecture notes in computer science, vol 3495. Springer, Berlin, pp 610–611
Hou Z, Gupta M, Nikiforuk P, Tan M (2007) A recurrent neural network for hierarchical control of interconnected dynamic systems. IEEE Trans Neural Netw 18:466–481. doi:10.1109/TNN.2006.885040
Pei Y, Ma J (2003) Real-time traffic flow data screening and reconstruction. China Civ Eng J 36:78–83
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This work is partially supported by Shandong Province Special development funds on Information Industry (No. 2006R00042).
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Sun, Z., Wang, Y. Traffic congestion identification by combining PCA with higher-order Boltzmann machine. Neural Comput & Applic 18, 417–422 (2009). https://doi.org/10.1007/s00521-009-0250-6
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DOI: https://doi.org/10.1007/s00521-009-0250-6