Freeway travel time prediction using takagi-sugeno-kang fuzzy neural network

Computer-Aided Civil and Infrastructure Engineering

Volumn 28, Issue 8, 2013, Pages 594-603

  Zhang, Yunlong ^a   Ge, Hancheng ^a  

^a Texas AandM University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; FUZZY LOGIC SYSTEM; HOUSTON , TEXAS; ONLINE COMPUTING; PREDICTION PERFORMANCE; TAKAGI-SUGENO-KANG FUZZY NEURAL NETWORKS; TIME SERIES MODELS; TRAVEL TIME PREDICTION;

FUZZY NEURAL NETWORKS; TIME VARYING CONTROL SYSTEMS;

FORECASTING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; FUZZY MATHEMATICS; MOTORWAY; PREDICTION; TRAVEL TIME;

TEXAS; UNITED STATES;

EID: 84880597745     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/mice.12014     Document Type: Article

References (35)

1. Adeli, H. & Jiang, X. (2003), Neuro-fuzzy logic model for freeway work zone capacity estimation, Journal of Transportation Engineering, 129(5), 484-93.
2. Al-Deek, H., D'Angelo, M. & Wang, M. (1998), Travel time prediction with non-linear time series, Proceedings of the ASCE 1998 5th International Conference on Applications of Advanced Technologies in Transportation, Newport Beach, CA, pp. 317-24.
3. Anderson, J., Bell, M., Sayers, T., Busch, F. & Heymann, G. (1994), The short-term prediction of link travel time in signal controlled road networks, Proceedings of the IFAC/IFORS 7th Symposium on Transportation Systems: Theory and Application of Advanced Technology, Tianjin, China, pp. 621-6.
4. Bhaskar, A., Chung, E. & Dumont, A. G. (2011), Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks, Computer-Aided Civil and Infrastructure Engineering, 26(6), 433-50.
5. Bianchini, A. (2012), Fuzzy representation of pavement condition for efficient pavement management, Computer-Aided Civil and Infrastructure Engineering, 27(8), 608-19.
6. Chen, P. S. T., Srinivasan, K. K., Mahmassani, H. S. (1999), Effect of information quality on compliance behavior of commuters under real-time traffic information, in Proceedings of the 77th Transportation Research Board Annual Meeting, National Academies Press, Washington DC, USA.
7. Chien, S., Ding, Y. & Wei, C. (2002), Dynamic bus arrival time prediction with artificial neural network, Journal of Transportation Engineering, 128(5), 429-38.
8. Chiu, S. (1994), Fuzzy model identification based on cluster estimation, Journal of Intelligent & Fuzzy Systems, 2(3), 267-78.
9. Dharia, A. & Adeli, H. (2003), Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16(7-8), 607-13.
10. Guin, A. (2006), Travel time prediction using a seasonal autoregressive integrated moving average time series model, in Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Canada, September 17-20.
11. Hamad, K., Lee, E., Shourijeh, M. T. & Faghri, A. (2009), Near-term travel time prediction utilizing Hilbert-Huang transform, Computer-Aided Civil and Infrastructure Engineering, 24(8), 551-76.
12. Heilmann, B., El Faouzi, N. E., de Mouzon, O., Hainitz, N., Koller, H., Bauder, D. & Antoniou, C. (2011), Predicting motorway traffic performance by data fusion of local sensor data and electronic toll collection data, Computer-Aided Civil and Infrastructure Engineering, 26(6), 451-63.
13. Hsiao, F. Y., Wang, S. S., Wang, W. C., Wen, C. P. & Yu, W. D. (2012), Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction, Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-81.
14. Jang, J-S. R. (1993), ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics, 23(18), 665-85.
15. Karim, A. & Adeli, H. (2002), Comparison of the fuzzy-wavelet RBFNN freeway incident detection model with the California Algorithm, Journal of Transportation Engineering, 128(1), 21-30.
16. Kreinovich, V., Quintana, C. & Reznik, L. (1992), Gaussian membership functions are most adequate in representing uncertainty in measurements, in Proceedings of NAFIPS'92: North American Fuzzy Information Processing Society Conference, Puerto Vallarta, Mexico, Vol. II, pp. 618-24.
17. Krikke, R. (2002), Short-range travel time prediction using an artificial neural network, in Proceedings of 9th World Congress on Intelligent Transport Systems, Chicago, Illinois, USA.
18. Li, Z., Yu, H., Liu, Y. & Liu, F. (2008), An improved adaptive exponential smoothing model for short-term travel time forecasting of urban arterial street, Acta Automatica Sinica, 34(11), 1404-9.
19. Liu, H., Zhang, K., He, R. & Li, J. (2009), A neural network model for travel time prediction, in Proceedings of the IEEE International Conference Intelligent Computing and Intelligent Systems, Shanghai, China.
20. Oda, T. (1990), An algorithm for prediction of travel time using vehicle sensor data, in Proceedings of the IEE 3rd International Conference on Road Traffic Control, London, pp. 40-4.
21. Park, D. & Rilett, R. (1998), Forecasting multiple-period freeway link travel time using modular neural network, Transportation Research Record, No. 98-0743, TRB, National Research Council, Washington DC.
22. Park, D. & Laurence, R. (1999), Forecasting freeway link travel times with a multilayer feedforward neural network. Computer-Aided Civil and Infrastructure Engineering, 14, 357-67.
23. Prasad, V. P. & Nelson, P. C. (1999), Application of fuzzy logic and neural networks for dynamic travel time estimation, International Transactions in Operational Research, 6, 145-60.
24. Rilett, L. & Park, D. (1999), Direct forecasting of freeway corridor travel times using spectral basis neural networks, Presented at the 78th TRB Annual Meeting (CD-ROM), Washington DC.
25. Samant, A. & Adeli, H. (2001), Enhancing neural network incident detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
26. Singh, A. & Abu-Lebdeh, G. (2007), Arterial network travel time estimation using conditional independence graphs and state space neural networks, in Proceedings of the 18th IASTED Conference on Modelling and Simulation, Montreal, Quebec, Canada.
27. Srinivasan, D., Choy, M. & Cheu, R. (2006), Neural networks for real-time traffic signal control, IEEE Transactions on Intelligent Transportation Systems, 7(3), 261-71.
28. Tagherouit, W. B., Bengassem, J. & Bennis, S. (2011), A fuzzy expert system for prioritizing rehabilitation sewer networks, Computer-Aided Civil and Infrastructure Engineering, 26(2), 146-52.
29. Takagi, T. & Sugeno, M. (1985), Fuzzy identification of systems and its applications to modeling and control. IEEE Trans actions on Systems, Man, and Cybernetics, 15(1), 116-32.
30. Van Lint, J. W. C. (2006), Reliable real-time framework for short-term freeway travel time prediction. Journal of Transportation Engineering, 132(12), 921-32.
31. Van Lint, J. W. C., Hoogendoorn, S. P. & Van Zuyle, H. J. (2005), Accurate freeway travel time prediction with state-space neural networks under missing data, Transportation Research Part C, 13, 347-69.
32. Wu, C. H., Ho, J. M. & Lee, D. T. (2004), Travel time prediction with support vector regression. IEEE Transactions on Intelligent Transportation System, 5(4), 276-81.
33. Yeon, J., Elefteriadou, L. & Lawphongpanich, S. (2008), Travel time estimation on a freeway using discrete time Markov chain, Transportation Research Part B 42, 325-38.
34. You, J. & Kim, J. (2000), Development and evaluation of a hybrid travel time forecasting model. Transportation Research Part C 8, 231-56.
35. Zhang, X. & Rice, J. (2003), Short-term travel time prediction. Transportation Research Part C 11(3-4), 187-210.