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Mathematical Problems in Engineering
Volume 2017, Article ID 1615691, 14 pages
https://doi.org/10.1155/2017/1615691
Research Article

Study on Reverse Reconstruction Method of Vehicle Group Situation in Urban Road Network Based on Driver-Vehicle Feature Evolution

1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China
2State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
3Shandong Zibo Experimental High School, Zibo 255000, China

Correspondence should be addressed to Xiaoyuan Wang; nc.ude.tuds@nauyoaixgnaw

Received 28 December 2016; Accepted 26 January 2017; Published 20 February 2017

Academic Editor: Nicolas Hudon

Copyright © 2017 Xiaoyuan Wang 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. X. Wang, J. Liu, and J. Zhang, “Dynamic recognition model of driver's propensity under multilane traffic environments,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 309415, 15 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Käfer, C. Hermes, C. Wöhler, F. Kummert, and H. Ritter, “Recognition and prediction of situations in urban traffic scenarios,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 4234–4237, IEEE, Istanbul, Turkey, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Meyer-Delius, C. Plagemann, and W. Burgard, “Probabilistic situation recognition for vehicular traffic scenarios,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '09), pp. 459–464, IEEE, Kobe, Japan, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Zhang, X. Wang, J. Wang, and X. Ban, “Vehicle group relationship transformation mechanism under dynamic and complex three-lane conditions,” Advances in Mechanical Engineering, vol. 7, no. 2, Article ID 687517, 2015. View at Google Scholar · View at Scopus
  5. Z. Sun and J. Ban, “Vehicle trajectory reconstruction for signalized intersections using mobile traffic sensors,” Transportation Research C, vol. 36, pp. 268–283, 2013, Proceedings of the 91st Transportation Research Board Annual Meeting, 2012. View at Publisher · View at Google Scholar
  6. X. Ban and M. Gruteser, “Towards fine-grained urban traffic knowledge extraction using mobile sensing,” in Proceedings of the International Workshop on Urban Computing (UrbComp '12)—Held in Conjunctionwith (KDD '12), pp. 111–117, Beijing, China, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Wang, J. Wang, J. Zhang, and J. Wang, “Dynamic recognition of driver’s propensity based on GPS mobile sensing data and privacy protection,” Mathematical Problems in Engineering, vol. 2016, Article ID 1814608, 12 pages, 2016. View at Publisher · View at Google Scholar
  8. X. Wang, J. Zhang, and L. Ma, “Estimation of gamma distribution shape parameter adapting to traffic flow evolvement,” Computer Engineering & Applications, vol. 50, no. 5, pp. 247–251, 2014. View at Google Scholar
  9. Y. Jiang, S. Han, W. Ting, and G. Zhang, “Analysis of vehicle arrival law at signalized intersection,” Journal of Transportation Engineering & Information, vol. 13, no. 1, pp. 1–6, 2015. View at Google Scholar
  10. M. Li, H. Lu, and C. Nie, “Urban road capacity algorithm based on multi-variant gamma distribution,” Highway Engineering, vol. 34, no. 4, pp. 136–140, 2009. View at Google Scholar
  11. J. Zhang, X. Wang, C. Yu, Z. Liu, and H. Wang, “Development of a prediction method for driver's propensity,” Procedia Engineering, vol. 137, pp. 161–170, 2016. View at Google Scholar
  12. M. Wistrand and E. L. L. Sonnhammer, “Improving profile HMM discrimination by adapting transition probabilities,” Journal of Molecular Biology, vol. 338, no. 4, pp. 847–854, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. J. C. Herrera, D. B. Work, R. Herring, X. Ban, Q. Jacobson, and A. M. Bayen, “Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century, field experiment,” Transportation Research Part C Emerging Technologies, vol. 18, no. 4, pp. 568–583, 2009. View at Google Scholar
  14. M. M. Al-Sayed, S. Khattab, and F. A. Omara, “Prediction mechanisms for monitoring state of cloud resources using Markov chain model,” Journal of Parallel and Distributed Computing, vol. 96, pp. 163–171, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. G. A. Pavliotis and A. M. Stuart, Multiscale methods, vol. 53 of Texts in Applied Mathematics, Springer, New York, NY, USA, 2008. View at MathSciNet