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Mathematical Problems in Engineering
Volume 2014, Article ID 184632, 8 pages
http://dx.doi.org/10.1155/2014/184632
Research Article

Research on Short-Term Traffic Flow Prediction Method Based on Similarity Search of Time Series

1State Key Laboratory of Automobile Simulation and Control, School of Traffic, Jilin University, Changchun 130025, China
2College of Transportation, Jilin University, Changchun 130025, China
3Jilin Province Key Laboratory of Road Traffic, College of Transportation, Jilin University, Changchun 130025, China
4College of Mechanical Science and Engineering, Jilin University, Changchun 130025, China

Received 3 June 2014; Accepted 31 July 2014; Published 18 August 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Zhaosheng Yang 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. M. Hamed, H. R. Al-Masaeid, and Z. M. Bani Said, “Short-term prediction of traffic volume in urban arterials,” Journal of Transportation Engineering, vol. 121, no. 3, pp. 249–254, 1995. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Yorgos and G. Stephanedes, “Improved estimation of traffic flow for real time control,” Transportation Research Record, vol. 795, pp. 28–39, 1981. View at Google Scholar
  3. H. Nicholson and C. D. Swann, “The prediction of traffic flow volumes based on spectral analysis,” Transportation Research, vol. 8, no. 6, pp. 533–538, 1974. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Stathopoulos and M. G. Karlaftis, “A multivariate state space approach for urban traffic flow modeling and prediction,” Transportation Research C: Emerging Technologies, vol. 11, no. 2, pp. 121–135, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Hu, C. Zong, J. Zhang et al., “An applicable short-term traffic flow forecasting method based on chaotic theory,” Intelligent Transportation Systems, vol. 1, no. 1, pp. 608–613, 2003. View at Google Scholar
  6. J. Wang and Q. Shi, “Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory,” Transportation Research Part C: Emerging Technologies, vol. 27, no. 1, pp. 219–232, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zhang and Z. Ye, “Short-term traffic flow forecasting using fuzzy logic system methods,” Journal of Intelligent Transportation Systems, vol. 12, no. 3, pp. 102–112, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Xie and Y. Zhang, “A wavelet network model for short-term traffic volume forecasting,” Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, vol. 10, no. 3, pp. 141–150, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  9. E. Castillo, J. M. Menéndez, and S. Sánchez-Cambronero, “Predicting traffic flow using Bayesian networks,” Transportation Research B: Methodological, vol. 42, no. 5, pp. 482–509, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Wei and M. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research C: Emerging Technologies, vol. 21, no. 1, pp. 148–162, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Kumar, M. Parida, and V. K. katiyar, “Short term traffic flow prediction for a non urban highway using artificial neural network,” Procedia-Social and Behavioral Sciences, vol. 104, pp. 755–764, 2013. View at Google Scholar
  12. H. B. Yin, S. C. Wong, J. Xu, and C. K. Wong, “Urban traffic flow prediction using a fuzzy-neural approach,” Transportation Research C: Emerging Technologies, vol. 10, no. 2, pp. 85–98, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Pushkar, F. L. Hall, and J. A. Acha-Daza, “Estimation of speeds from single-loop freeway flow and occupancy data using cusp catastrophe theory model,” Transportation Research Record, no. 1457, pp. 149–157, 1994. View at Google Scholar · View at Scopus
  14. S. Clark, “Traffic prediction using multivariate nonparametric regression,” Journal of Transportation Engineering, vol. 129, no. 2, pp. 161–168, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. I. Okutani and Y. J. Stephanedes, “Dynamic prediction of traffic volume through Kalman filtering theory,” Transportation Research B, vol. 18, no. 1, pp. 1–11, 1984. View at Google Scholar · View at Scopus
  16. J. Whittaker, S. Garside, and K. Lindveld, “Tracking and predicting a network traffic process,” International Journal of Forecasting, vol. 13, no. 1, pp. 51–61, 1997. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Min and L. Wynter, “Real-time road traffic prediction with spatio-temporal correlations,” Transportation Research C: Emerging Technologies, vol. 19, no. 4, pp. 606–616, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Kamarianakis, H. Oliver Gao, and P. Prastacos, “Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions,” Transportation Research C: Emerging Technologies, vol. 18, no. 5, pp. 821–840, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Agrawal, C. Faloutsos, and A. Swami, “Efficient similarity search in sequence databases,” in Proceedings of the 4th International Conference of Foundation of Data Organization and Algorithms (FODO '93), pp. 69–84, Chicago, Ill, USA, 1993.
  20. K. Chan and A. W. Fu, “Efficient time series matching by wavelets,” in Proceedings of the 15th International Conference on Data Engineering (ICDE '99), pp. 126–133, Sydney, Australia, March 1999. View at Scopus
  21. E. Keogh, K. Chakrabarti, S. Mehrotra, and M. Pazzani, “Locally adaptive dimensionality reduction for indexing large time series databases,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 151–162, Santa Barbara, Calif, USA, May 2001. View at Scopus
  22. J. Lin, E. Keogh, L. Wei, and S. Lonardi, “Experiencing SAX: a novel symbolic representation of time series,” Data Mining and Knowledge Discovery, vol. 15, no. 2, pp. 107–144, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  23. E. J. Keogh and M. J. Pazzani, “An indexing scheme for fast similarity search in large time series databases,” in Proceedings of the 11th International Conference on Scientific and Statistical Database Management (SSDBM '99), pp. 56–67, Cleveland, Ohio, USA, July 1999. View at Publisher · View at Google Scholar · View at Scopus
  24. C. S. Perng, H. Wang, S. R. Zhang, and D. S. Parker, “Landmarks: a new model for similarity-based pattern querying in time series databases,” in Proceedings of the IEEE 16th International Conference on Data Engineering (ICDE '00), pp. 33–42, San Diego, Calif , USA, March 2000. View at Scopus
  25. H. Jaeger and H. Haas, “Harnessing nonlinearity: prediction of chaotic time series with neural networks,” Science, vol. 304, no. 5667, pp. 78–80, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Computer Science Review, vol. 3, no. 3, pp. 127–149, 2009. View at Publisher · View at Google Scholar · View at Scopus