Abstract
The speed prediction algorithm introduced in this paper takes advantage of fuzzy systems that are insensitive to random noise, robust to uncertainties, and transparent to interpretation. The proposed algorithm for outlier detection selects the potential outliers based on the density rather than the deviation adopted in conventional approaches. To evaluate the developed system, a seris of experiments conducted on the real world data. The result of the comparison performed to evaluate the outliler detection method proposed reveals the benefit from the consideration of density. The cross validation results indicate the effectiveness of the fuzzy inference system developed.
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References
Chung, E., Warita, H., Bajwa, S., Kuwahara, M.: Travel time prediction: issues and benefits. In: 10th World Conference on Transportation Research (2004)
Wu, C.H., Ho, J.M., Lee, D.T.: Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems 5(4), 276–281 (2004)
Asce, S.I.M., Asce, H.A.D.M.: Performance Evaluation of Short-Term Time-Series Traffic Prediction Model. Journal of Transportation Engineering 128, 490–498 (2002)
Asce, H.A.D.M., Asce, L.A.H.F.: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering 129, 664 (2003)
Rice, J., van Zwet, E.: A simple and effective method for predicting travel times on freeways. IEEE Transactions on Intelligent Transportation Systems 5(3), 200–207 (2004)
Bajwa, S., Chung, E., Kuwahara, M.: Performance evaluation of an adaptive travel time prediction model. In: IEEE Conference on Intelligent Transportation Systems, pp. 1000–1005 (2005)
Innamaa, S.: Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway. Transportation 32(6), 649–669 (2005)
van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks. Transportation Research Record 1811(1), 30–39 (2002)
Quek, C., Pasquier, M., Lim, B.B.S.: POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems 7(2), 133–146 (2006)
**ao, H., Sun, H., Ran, B., Oh, Y.: Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition. Transportation Research Record 1836(1), 16–20 (2003)
Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1(1) (1993)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2(12), pp. 1137–1143 (1995)
Rousseeuw, P.J., Ruts, I., Tukey, J.W.: The Bagplot: A Bivariate Boxplot. The American Statistician 53(4) (1999)
He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recognition Letters 24(9-10), 1641–1650 (2003)
Fu, L., Medico, E.: FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics 8, 3 (2007)
Wu, S., Er, M.J., Gao, Y.: A fast approach for automatic generation of fuzzy rules bygeneralized dynamic fuzzy neural networks. IEEE Transactions on Fuzzy Systems 9(4), 578–594 (2001)
Casillas, J., Carse, B., Bull, L.: Fuzzy-XCS: A Michigan Genetic Fuzzy System. IEEE Transactions on Fuzzy Systems 15(4), 536–550 (2007)
Papadakis, S.E., Theocharis, J.B.: A GA-based fuzzy modeling approach for generating TSK models. Fuzzy Sets and Systems 131(2), 121–152 (2002)
Mitra, S., Konwar, K.M., Pal, S.K.: Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation. IEEE Transactions on Systems, Man and Cybernetics, Part C 32(4), 328–339 (2002)
Kim, M.W., Lee, J.G., Min, C.: Efficient fuzzy rule generation based on fuzzy decision tree for data mining. In: The 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE 1999 (1999)
Abonyi, J., Szeifert, F.: Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters 24(14), 2195–2207 (2003)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
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Wang, Y., Liu, H., Beullens, P., Brown, D. (2008). Travel Speed Prediction Using Fuzzy Reasoning. In: **ong, C., Huang, Y., **ong, Y., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science(), vol 5314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88513-9_48
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DOI: https://doi.org/10.1007/978-3-540-88513-9_48
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