Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 603274, 9 pages
http://dx.doi.org/10.1155/2014/603274
Research Article

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

School of Highway, Chang’an University, Xi’an 710064, China

Received 24 January 2014; Accepted 19 February 2014; Published 27 March 2014

Academic Editors: X. Meng and J. Zhou

Copyright © 2014 Longfei 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. E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Temporal evolution of short-term urban traffic flow: a nonlinear dynamics approach,” Computer-Aided Civil and Infrastructure Engineering, vol. 23, no. 7, pp. 536–548, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. Z. Hesheng, Z. Yi, and H. Dongcheng, “Study on method of traffic state analysis for urban traffic network,” Intelligent Transportation System, vol. 1, pp. 23–27, 2006. View at Google Scholar
  3. E. Azimirad, N. Pariz, and M. B. N. Sistani, “A novel fuzzy model and control of single intersection at urban traffic network,” IEEE Systems Journal, vol. 4, no. 1, pp. 107–111, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Shen and Z. Y. Gao, “Dynamical properties of transportation on complex networks,” Physica A: Statistical Mechanics and Its Applications, vol. 387, no. 5-6, pp. 1352–1360, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Chen, Y. Zhang, and J. Hu, “Multi-dimensional traffic flow time series analysis with self-organizing maps,” Tsinghua Science and Technology, vol. 13, no. 2, pp. 220–228, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Kohonen, “Self-organizing maps of massive databases,” International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, vol. 9, no. 4, pp. 179–185, 2001. View at Google Scholar · View at Scopus
  7. 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
  8. H. L. Duan, Z. H. Li, L. Li, Y. Zhang, and S. C. Yin, “Network-wide traffic state observation and analysis method using pseudo-color map,” Journal of Transportation Systems Engineering and Information Technology, vol. 9, no. 4, pp. 46–52, 2009. View at Google Scholar · View at Scopus
  9. M. Treiber and A. Kesting, “Validation of traffic flow models with respect to the spatiotemporal evolution of congested traffic patterns,” Transportation Research C: Emerging Technologies, vol. 21, no. 1, pp. 31–41, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. L. W. Lan, J. B. Sheu, and Y. S. Huang, “Investigation of temporal freeway traffic patterns in reconstructed state spaces,” Transportation Research C: Emerging Technologies, vol. 16, no. 1, pp. 116–136, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. B. S. Kerner, H. Rehborn, M. Aleksic, and A. Haug, “Recognition and tracking of spatial-temporal congested traffic patterns on freeways,” Transportation Research C: Emerging Technologies, vol. 12, no. 5, pp. 369–400, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. Q. Q. Li, D. Q. Gao, and B. S. Yang, “Urban road traffic status classification based on fuzzy support vector machines,” Journal of Jilin University (Engineering and Technology Edition), vol. 39, no. 2, pp. 131–134, 2009. View at Google Scholar · View at Scopus
  13. Z. H. Li, D. Sun, X. X. Jin, D. Yu, and Z. Zhang, “Pattern-based study on urban transportation system state classification and properties,” Journal of Transportation Systems Engineering and Information Technology, vol. 8, no. 5, pp. 83–87, 2008. View at Google Scholar · View at Scopus
  14. Z. H. Li, D. Sun, X. X. Jin, D. Yu, and Z. Zhang, “Pattern-based study on urban transportation system state and properties with fuzzy reasoning methods,” Journal of Transportation Systems Engineering and Information Technology, vol. 8, no. 5, pp. 83–87, 2008. View at Google Scholar · View at Scopus
  15. A. Lozano, G. Manfredi, and L. Nieddu, “An algorithm for the recognition of levels of congestion in road traffic problems,” Mathematics and Computers in Simulation, vol. 79, no. 6, pp. 1926–1934, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Montazeri-Gh and A. Fotouhi, “Traffic condition recognition using the k-means clustering method,” Scientia Iranica, vol. 18, no. 4, pp. 930–937, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Yudong, Z. Yi, H. Jianming, and Y. Danya, “Pattern discovering of regional traffic status with self-organizing maps,” in Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC '06), pp. 647–652, September 2006. View at Scopus
  18. M. Treiber, A. Kesting, and R. E. Wilson, “Reconstructing the traffic state by fusion of heterogeneous data,” Computer-Aided Civil and Infrastructure Engineering, vol. 26, no. 6, pp. 408–419, 2011. View at Publisher · View at Google Scholar · View at Scopus