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Volume 6 Issue 6
Nov.  2019

IEEE/CAA Journal of Automatica Sinica

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Runmei Li, Yinfeng Huang and Jian Wang, "Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344-1351, Nov. 2019. doi: 10.1109/JAS.2019.1911723
Citation: Runmei Li, Yinfeng Huang and Jian Wang, "Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344-1351, Nov. 2019. doi: 10.1109/JAS.2019.1911723

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

doi: 10.1109/JAS.2019.1911723
Funds:  This work was supported by the National Key Research and Development Program of China (2018YFB1201500)
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  • This paper uses Gaussian interval type-2 fuzzy set theory on historical traffic volume data processing to obtain a 24- hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function. Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.


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  • [1]
    Z. Hou and X. Li, " Repeatability and similarity of freeway traffic flow and long-term prediction under big data,” IEEE Trans. Intelligent Transportation Systems, vol. 17, no. 6, pp. 1786–1796, 2016. doi: 10.1109/TITS.2015.2511156
    K. Xie and R. Li, " A combined forecasting method for traffic volume,” in Proc. IEEE Int. Conf. Service Operations & Logistics, IEEE, Beijing, China, 2016.
    I. Lana, J. D. Ser, and I. I. Olabarrieta, " Understanding daily mobility patterns in urban road networks using traffic flow analytics,” in Proc. Network Operations & Management Symposium, IEEE, Istanbul, Turkey, 2016.
    T. Thomas, W. Weijermars, and E. V. Berkum, " Predictions of urban volumes in single time series,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 1, pp. 71–80, 2010. doi: 10.1109/TITS.2009.2028149
    X. Jiang and H. Adeli, " Dynamic wavelet neural network model for traffic flow forecasting,” J. Transportation Engineering-asce, vol. 131, pp. 10, 2005.
    G. Yan and G.-J Wang, Study of Traffic Flow Short-Time Prediction Based on Wavelet Neural Network. Berlin, Heidelberg, Germany: Springer, pp. 509–516, 2011.
    S. Oh, Y. Kim, and J. Hong, " Urban traffic flow prediction system using a multifactor pattern recognition model.,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 5, pp. 2744–2755, 2015. doi: 10.1109/TITS.2015.2419614
    H. Yi, P. Edara, and C. Sun, " Traffic flow forecasting for urban work zones,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 4, pp. 1761–1770, 2015. doi: 10.1109/TITS.2014.2371993
    H. Liu, X. M. Shi, D. M. Guo, Z. W. Zhao, and Yimin, " Feature selection combined with neural network structure optimization for hiv-1 protease cleavage site prediction,” Biomed Research Int., vol. 2015, no. 1, pp. 1–11, 2015.
    B. Sharma, V. K. Katiyar, and A. K. Gupta, " Fuzzy logic model for the prediction of traffic volume in week days,” Int. J. Computer Applications, vol. 107, no. 17, 2014.
    R. Li, C. Jiang, F. Zhu, and X. Chen, " Traffic flow data forecasting based on interval type-2 fuzzy sets theory,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 2, pp. 141–148, 2016. doi: 10.1109/JAS.2016.7451101
    R. A. Aliev, W. Pedrycz, B. G. Guirimov, R. R. Aliev, U. Ilhan, M. Babagil, and S. Mammadli, " Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization,” Information Sciences, vol. 181, no. 9, pp. 1591–1608, 2011. doi: 10.1016/j.ins.2010.12.014
    E. Rubio, O. Castillo, F. Valdez, P. Melin, C. I. Gonzalez, and G. Martinez, " An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques,” Advances in Fuzzy Systems, vol. 2017, 2017.
    C. I. Gonzalez, P. Melin, J. R. Castro, O. Mendoza, and O. Castillo, " An improved sobel edge detection method based on generalized type-2 fuzzy logic,” Soft Computing, vol. 20, no. 2, pp. 773–784, 2016. doi: 10.1007/s00500-014-1541-0
    M. Pulido, P. Melin, and O. Mendoza, " Particle swarm optimization of ensemble neural networks with type-1 and type-2 fuzzy integration for the taiwan stock exchange, ” in Proc. Nature-Inspired Design of Hybrid Intelligent Systems, Springer, Cham, Switzerland, pp. 409–421, 2017.
    J. M. Mendel, " Uncertainty, fuzzy logic, and signal processing,” Signal Processing, vol. 80, no. 6, pp. 913–933, 2000. doi: 10.1016/S0165-1684(00)00011-6
    M. A. Khanesar, M. Teshnehlab, E. Kayacan, and O. Kaynak, " A novel type-2 fuzzy membership function: application to the prediction of noisy data,” in Proc. IEEE Int. Conf. Computational Intelligence for Measurement Systems and Applications, IEEE, Ottawa, Canada, pp. 128–133, 2010.
    L. A. Zadeh, " The concept of a linguistic variable and its application to approximate reasoningi,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975. doi: 10.1016/0020-0255(75)90036-5
    M. Mizumoto and K. Tanaka, " Some properties of fuzzy sets of type 2,” Information and Control, vol. 31, no. 4, pp. 312–340, 1976. doi: 10.1016/S0019-9958(76)80011-3
    L. Yimin and H. Jing, " Type-2 fuzzy mathematical modeling and analysis of the dynamical behaviors of complex ecosystems,” Simulation Modelling Practice and Theory, vol. 16, no. 9, pp. 1379–1391, 2008. doi: 10.1016/j.simpat.2008.07.006
    Y. Li and X. Sun, " Modelling dynamic niche and community model by type-2 fuzzy set,” Ecological Modelling, vol. 211, no. 3–4, pp. 375–382, 2008. doi: 10.1016/j.ecolmodel.2007.09.018
    C. Glackin, L. Maguire, R. McIvor, P. Humphreys, and P. Herman, " A comparison of fuzzy strategies for corporate acquisition analysis,” Fuzzy Sets and Systems, vol. 158, no. 18, pp. 2039–2056, 2007. doi: 10.1016/j.fss.2007.03.020
    S. Greenfield and F. Chiclana, " Defuzzification of the discretised generalised type-2 fuzzy set: experimental evaluation,” Information Sciences, vol. 244, pp. 1–25, 2013. doi: 10.1016/j.ins.2013.04.032
    J. M. Mendel, " Computing with words and its relationships with fuzzistics,” Information Sciences, vol. 177, no. 4, pp. 988–1006, 2007. doi: 10.1016/j.ins.2006.06.008
    S. S. Gilan, M. H. Sebt, and V. Shahhosseini, " Computing with words for hierarchical competency based selection of personnel in construction companies,” Applied Soft Computing, vol. 12, no. 2, pp. 860–871, 2012. doi: 10.1016/j.asoc.2011.10.004
    N. N. Karnik and J. M. Mendel, " Applications of type-2 fuzzy logic systems to forecasting of time-series,” Information Sciences, vol. 120, no. 1–4, pp. 89–111, 1999. doi: 10.1016/S0020-0255(99)00067-5
    B.-I. Choi and F. C. -H. Rhee, " Interval type-2 fuzzy membership function generation methods for pattern recognition,” Information Sciences, vol. 179, no. 13, pp. 2102–2122, 2009. doi: 10.1016/j.ins.2008.04.009
    D. W. W. W. Tan, " A simplified type-2 fuzzy logic controller for realtime control,” ISA Trans., vol. 45, no. 4, pp. 503–516, 2006. doi: 10.1016/S0019-0578(07)60228-6
    T. Dereli, A. Baykasoglu, K. Altun, A. Durmusoglu, and I. B. Türksen, " Industrial applications of type-2 fuzzy sets and systems: A concise review,” Computers in Industry, vol. 62, no. 2, pp. 125–137, 2011. doi: 10.1016/j.compind.2010.10.006
    C. Leal-Ramírez, O. Castillo, P. Melin, and A. Rodríguez-Díaz, " Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure,” Information Sciences, vol. 181, no. 3, pp. 519–535, 2011. doi: 10.1016/j.ins.2010.10.011
    S. Chakravarty and P. K. Dash, " A pso based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices,” Applied Soft Computing, vol. 12, no. 2, pp. 931–941, 2012. doi: 10.1016/j.asoc.2011.09.013
    H. Mo, F.-Y. Wang, M. Zhou, R. Li, and Z. Xiao, " Footprint of uncertainty for type-2 fuzzy sets,” Information Sciences, vol. 272, pp. 96–110, 2014. doi: 10.1016/j.ins.2014.02.092
    J. MacQueen et al., " Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297,1967.
    V. Kreinovich, C. Quintana, and L. Reznik, " Gaussian membership functions are most adequate in representing uncertainty in measurements, ” in Proc. NAFIPS, vol. 92, pp. 15–17, 1992.
    D. Wu and J. M. Mendel, " Enhanced karnik-mendel algorithms,” IEEE Trans. Fuzzy Systems, vol. 17, no. 4, pp. 923–934, 2009. doi: 10.1109/TFUZZ.2008.924329


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    • We apply type-2 fuzzy sets theory in long-term traffic volume predictions. Simulation results indicate the method oposed in the paper gives full play to the ability of type-2 fuzzy set theory to deal with uncertainty data.
    • We propose a data-driven approach to construct the interval type-2 fuzzy sets which makes the construction of the sets more convincing. Meanwhile we transform traffic flow data into traffic states as to obtain the characteristics of traffic flow innovatively, which are ultimately used to construct the embedded type-1 fuzzy sets.
    • Finally we present a traffic volume forecasting model which can estimate the probability distribution of traffic volume at the same time horizon along with the highly accurate traffic volume prediction results.


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