Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2013, Article ID 195824, 7 pages
Research Article

Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy -Mean Clustering and Advanced Neural Network

1Shanghai Urban-Rural Construction and Transportation Development Institute, Shanghai 300032, China
2Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai 200240, China

Received 5 July 2013; Revised 8 October 2013; Accepted 9 October 2013

Academic Editor: Baocang Ding

Copyright © 2013 He Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M.-W. Li, W.-C. Hong, and H.-G. Kang, “Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm,” Neurocomputing, vol. 99, no. 1, pp. 230–240, 2013. View at Google Scholar
  2. T. Pohlmann and B. Friedrich, “A combined method to forecast and estimate traffic demand in urban networks,” Transportation Research C, vol. 31, pp. 131–144, 2013. View at Publisher · View at Google Scholar
  3. J. Abdi, B. Moshiri, B. Abdulhai, and A. K. Sedigh, “Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm,” Engineering Applications of Artificial Intelligence, vol. 25, no. 5, pp. 1022–1042, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Danech-Pajouh and M. Aron, “Athena: a method for short-term inter-urban motorway traffic forecasting,” Recherche Transports Sécurité, vol. 6, pp. 11–16, 1991. View at Google Scholar
  5. H.-W. Kim, J.-H. Lee, Y.-H. Choi, Y.-U. Chung, and H. Lee, “Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX,” Computer Communications, vol. 34, no. 1, pp. 99–106, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Wiley, 1976.
  7. Y. Kamarianakis and P. Prastacos, “Space-time modeling of traffic flow,” Computers and Geosciences, vol. 31, no. 2, pp. 119–133, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Yin, S. C. Wong, J. Xu, and C. K. Wong, “Urban traffic flow prediction using a fuzzy-neural approach,” Transportation Research Part C, vol. 10, no. 2, pp. 85–98, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Castro-Neto, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions,” Expert Systems with Applications, vol. 36, no. 3, pp. 6164–6173, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Guo, M. Liu, L. Yu, J. Guan, S. Guo, and X. Zhang, “Development and applications of macroscopic measurement of traffic congestion in Beijing,” in Proceedings of the 7th China Annual Conference on Intelligent Transportation Systems, Beijing, China, 2007.
  11. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, no. 2-3, pp. 191–203, 1984. View at Google Scholar · View at Scopus
  12. Z. Liu and Q. Liu, “Studying cost-sensitive learning for multi-class imbalance in Internet traffic classification,” The Journal of China Universities of Posts and Telecommunications, vol. 19, no. 6, pp. 63–72, 2012. View at Publisher · View at Google Scholar
  13. H. K. Lam, S. H. Ling, F. H. F. Leung, and P. K. S. Tam, “Tuning of the structure and parameters of neural network using an improved genetic algorithm,” in Proceedings of the 27th Annual Conference of the IEEE Industrial Electronics Society (IECON '2001), pp. 25–30, December 2001. View at Scopus
  14. J.-T. Tsai, J.-H. Chou, and T.-K. Liu, “Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 69–80, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Yu, G. Li, Y. Bai, and X. Jin, “Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO,” in Proceedings of the 26th Chinese Control Conference (CCC '07), pp. 228–232, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Q. Tang, D. Li, Y. Xi, and D. Yin, “Soft-sensing design based on semiclosed-loop framework,” Chinese Journal of Chemical Engineering, vol. 20, no. 6, pp. 1213–1218, 2012. View at Publisher · View at Google Scholar
  17. Q. F. Tang, The cooperative quantum-particle swarm algorithm and its application in the energy utilization optimization of the steam network [M.E. thesis], East China University of Science&Technology, China, 2011, (Chinese).