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Mathematical Problems in Engineering
Volume 2014, Article ID 607195, 8 pages
http://dx.doi.org/10.1155/2014/607195
Research Article

A Bayesian Combined Model for Time-Dependent Turning Movement Proportions Estimation at Intersections

School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received 1 April 2014; Revised 23 June 2014; Accepted 1 July 2014; Published 17 July 2014

Academic Editor: X. Zhang

Copyright © 2014 Pengpeng Jiao 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. Cremer and H. Keller, “Dynamic identification of O-D flows from traffic counts at complex intersections,” in Proceedings of the 8th International Symposium on Transportation and Traffic Theory, pp. 121–142, University of Toronto Press, Toronto, Canada, 1981.
  2. M. Cremer, “Determining the time dependent trip distribution in a complex intersection for traffic responsive control,” in Proceedings of the 4th International IFAC/IFIP/IFORS Conference on Control in Transportation Systems, Baden-Baden, Germany, 1983.
  3. M. Cremer and H. Keller, “A systems dynamics approach to the estimation of entry and exit O-D flows,” in Proceedings of the 9th International Symposium on Transportation and Traffic Theory, pp. 431–450, Vnu Science Press, Utrecht, The Netherlands, 1984. View at MathSciNet
  4. N. L. Nihan and G. A. Davis, “Recursive estimation of origin-destination matrices from input/output counts,” Transportation Research B, vol. 21, no. 2, pp. 149–163, 1987. View at Publisher · View at Google Scholar · View at Scopus
  5. M. G. H. Bell, “The estimation of origin-destination matrices by constrained generalised least squares,” Transportation Research B: Methodological, vol. 25, no. 1, pp. 13–22, 1991. View at Publisher · View at Google Scholar · View at MathSciNet
  6. B. Li and B. De Moor, “Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices,” Transportation Research B: Methodological, vol. 33, no. 3, pp. 203–214, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. P. P. Jiao, H. P. Lu, and L. Yang, “Real-time estimation of turning movement proportions based on genetic algorithm,” in Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, pp. 484–489, Vienna, Austria, September 2005. View at Scopus
  8. I. Okutani, “The kalman filtering approaches in some transportation and traffic problems,” in Proceedings of the 10th International Symposium on Transportation and Traffic Theory, pp. 397–416, Elsevier Science, New York, NY, USA, 1987.
  9. K. Ashok and M. E. Ben-Akiva, “Alternative approaches for real-time estimation and prediction of time-dependent origin-destination flows,” Transportation Science, vol. 34, no. 1, pp. 21–36, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Ashok and M. E. Ben-Akiva, “Estimation and prediction of time-dependent origin-destination flows with a stochastic mapping to path flows and link flows,” Transportation Science, vol. 36, no. 2, pp. 184–198, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  11. P. W. Lin and G. L. Chang, “A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix,” Transportation Research B: Methodological, vol. 41, no. 5, pp. 554–572, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. J. W. Li, B. L. Lin, Z. H. Sun, and X. F. Geng, “An estimation model of time-varying origin-destination flows in expressway corridors based on unscented Kalman filter,” Science in China E: Technological Sciences, vol. 52, no. 7, pp. 2069–2078, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Nie and H. M. Zhang, “A variational inequality formulation for inferring dynamic origin-destination travel demands,” Transportation Research B: Methodological, vol. 42, no. 7-8, pp. 635–662, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Lou and Y. Yin, “A decomposition scheme for estimating dynamic origin-destination flows on actuation-controlled signalized arterials,” Transportation Research C, vol. 18, no. 5, pp. 643–655, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. C. C. Lu, X. Zhou, and K. Zhang, “Dynamic origin-destination demand flow estimation under congested traffic conditions,” Transportation Research C: Emerging Technologies, vol. 34, pp. 16–37, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Zheng, D. H. Lee, and Q. Shi, “Short-term freeway traffic flow prediction: bayesian combined neural network approach,” Journal of Transportation Engineering, vol. 132, no. 2, pp. 114–121, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Dong, R. M. Li, L. G. Sun, T. H. Chang, and H. P. Lu, “Short-term traffic forecast system of Beijing,” Transportation Research Record, no. 2193, pp. 116–123, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Wen, L. Zhou, X. Li, and B. W. Zhang, Neural Network Simulation and Application Using Matlab [Monograph], Science Press, 2003.
  19. Y. Lin, Y. L. Cai, and Y. X. Huang, “Kalman filtering based dynamic OD matrix estimation and prediction for traffic systems,” in Proceedings of the 6th International IEEE Conference on Intelligent Transportation Systems, vol. 2, pp. 1515–1520, Shanghai, China, 2003.
  20. D. J. C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge, UK, 2003. View at MathSciNet