Traffic flow forecasting neural networks based on exponential smoothing method

Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011

Volumn , Issue , 2011, Pages 376-381

  Chan, K Y ^a   Dillon, T S ^a   Singh, J ^a   Chang, E ^a  

^a CURTIN UNIVERSITY   (Australia)

Author keywords

exponential smoothing; neural network; traffic flow forecasting

Indexed keywords

EXPONENTIAL SMOOTHING; EXPONENTIAL SMOOTHING METHOD; GENERALIZATION CAPABILITY; NETWORK DEVELOPMENT; NEURAL NETWORK MODEL; NEURAL NETWORK TRAINING; NON-SMOOTH; REAL-TIME TRAFFIC CONDITIONS; TESTING ERRORS; TRAFFIC FLOW; TRAFFIC FLOW FORECASTING; TRAINING ERRORS; TRAINING PURPOSE; WESTERN AUSTRALIA;

ERRORS; FORECASTING; INDUSTRIAL ELECTRONICS;

NEURAL NETWORKS;

EID: 80052219807     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIEA.2011.5975612     Document Type: Conference Paper

References (18)

1. Arem, V.B., Kirby, H.R., Van Der Vlist, M.J.M., Whittaker, J.C.: Recent advances and applications in the field of short-term traffic forecasting. International Journal of Forecasting, 13, 1-12 (1997)
2. Henry, C.C., Rujirek, B.: Forecasting Thailand's rice export: statistical techniques vs. artificial neural networks. Computers and Industrial Engineering, 53, 610-627 (2007)
3. DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, Department of Computer and Communication Science, University of Michigan (1975)
4. De Lurgio, S: Forecasting principles and applications. McGraw Hill, Inc, New York (1998).
5. Dillon, T.S., Sestito, S., Leung, S.: Short term load forecasting using an adaptive neural network. International Journal of Electrical Power and Energy System, 13(4), 186-192, 1991.
6. Faria, E.L., Albuquerque, M.P., Gonzalez, J.L., Cavalcanta, J.T.P., Albuquerque, M.P.: Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36, 12506-12509 (2009)
7. Gutierrez, R.S., Adriano, O.S., Mukhopadhyay, S.: Lumpy demand forecasting using neural networks. International Journal of Production Economics, 111, 409-420 (2008)
8. Kirby, H.R., Watson, S.M., Dougherty, M.S.: Should we use neural networks or statistical models for short-term motorway traffic forecasting. International Journal of Forecasting, 13, 43-50 (1997)
9. Prechelt L., Automatic early stopping using cross validation: quantifying the criteria, Neural Networks, vol. 11, no. 4, pp. 761-777, 1998.
10. Quek, C., Pasquier, M. and 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)
11. Qi, M., Zhang, P.: Trend time-series modeling and forecasting with neural networks. IEEE Transactions on Neural Networks, 19(5), 808-816-337 (2008)
12. Tan, M.C., Wong, S.C., Xu, J.M., Guan, Z.R., Zhang, P.: An aggregation approach to short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 10(1), 60-69 (2009)
13. Widrow, B., Lehr, M.A.: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78(9), 1415-1442 (1990)
14. Xue, D., Dong, Z.: An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition and optimization. IEEE Transactions on Control Systems Technology, 8(1) (2000)
15. Xu, W., He, S., Song, R., Li, J.: Reliability based assignment in stochastic flow freight network. Applied Mathematics and Computation, 211(1), 85-94 (2009)
16. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transportation Research Part C: Emerging Technologies, 13(3), 211-234 (2005)
17. Yaffee, R. A., McGee, M., Introduction to Time Series Analysis and Forecasting. San Diego, CA: Academic (2000)
18. Setiono R., Feedforward neural network construction using cross validation, Neural Computation, vol. 13, no. 12, pp. 2865-2977, 2001.