Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2014, Article ID 892132, 11 pages
http://dx.doi.org/10.1155/2014/892132
Research Article

A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

1Transportation School, Southeast University, 2 Sipailou, Nanjing 210096, China
2School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
3Highway School, Chang’an University, The Middle Section of Southern Second Ring Road, Xi’an 710064, China

Received 14 July 2014; Accepted 5 October 2014; Published 4 November 2014

Academic Editor: Xiaobei Jiang

Copyright © 2014 Pengfei Li 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. D. C. J. Chang, L. Blincoe, R. Subramanian, and L. Lombardo, “CICAS-V research on comprehensive costs of intersection crashes,” Tech. Rep. 07-0016, National Highway Traffic Safety Administration, Washington, DC, USA, 2007. View at Google Scholar
  2. D. R. RagLand and A. A. Zabyshny, “Intersection decision support project: taxonomy of crossing-path crashes at intersections using GES 2000 data,” UCB-TSC-RR-2003-08, University of California, Berkeley, Calif, USA, 2000. View at Google Scholar
  3. L. Zhang, L. Wang, K. Zhou, and W. B. Zhang, “Dynamic all-red extension at a signalized intersection: a framework of probabilistic modeling and performance evaluation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 166–179, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Zhang, K. Zhou, W.-B. Zhang et al., Empirical Observations of Red Light Running at Arterial Signalized Intersection, Institute of Transportation Studies, University of California, Berkeley, California PATH, 2008.
  5. T. M. Mitchell, Machine Learning, WCB/McGraw-Hill, Portland, Ore, USA, 1997.
  6. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biology, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. F. Rosenblatt, The Perceptron, a Perceiving and Recognizing Automaton Project Para, Cornell Aeronautical Laboratory, 1957.
  8. F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books, Washington, DC, USA, 1961. View at MathSciNet
  9. P. J. Werbos, Beyond regression: new tools for prediction and analysis in the behavioral sciences [Ph.D. thesis], Hardward University, Cambridge, Mass, USA, 1974.
  10. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Publisher · View at Google Scholar · View at Scopus
  11. M. I. Jordan, “Serial order: a paralle distributed processing approach,” Institute for Cognitive Science Report 8604, San Diego, Calif, USA, 1986. View at Google Scholar
  12. J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, 1990. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Fahlman, “Faster-learning variations on back-propagation: an empirical study,” in Proceedings of the Connectionist Models Summer School, Morgan-Kaufmann, 1988.
  14. M. Riedmiller and H. Braun, “A direct adaptive method for faster backpropagation learning: the RPROP algorithm,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 586–591, San Francisco, Calif, USA, April 1993. View at Scopus
  15. S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural Information Processing Systems 2, S. T. David, Ed., pp. 524–532, Morgan Kaufmann Publishers, Boston, Mass, USA, 1990. View at Google Scholar
  16. J. Lu, S. Chen, W. Wang, and B. Ran, “Automatic traffic incident detection based on nFOIL,” Expert Systems with Applications, vol. 39, no. 7, pp. 6547–6556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Huang, “Traffic flow forecasting based on wavelet neural network and support vector machine,” Journal of Computational Information Systems, vol. 8, no. 8, pp. 3471–3478, 2012. View at Google Scholar · View at Scopus
  18. L. Chong, M. M. Abbas, and A. Medina, “Simulation of driver behavior with agent-based back-propagation neural network,” Transportation Research Record, no. 2249, pp. 44–51, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Jia, Z. Juan, and A. Ni, “Develop a car-following model using data collected by ‘five-wheel system‘,” in Proceedings of the Intelligent Transportation Systems, IEEE, 2003.
  20. S. Panwai and H. Dia, “Neural agent car-following models,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 60–70, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Zhang, K. Zhou, W.-B. Zhang, and J. A. Misener, “Prediction of red light running based on statistics of discrete point sensors,” Transportation Research Record, vol. 2128, pp. 132–142, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Li and M. Abbas, “Stochastic dilemma hazard model at high-speed signalized intersections,” Journal of Transportation Engineering, vol. 136, no. 5, pp. 448–456, 2010. View at Google Scholar · View at Scopus
  23. Wavetronix LLC, Wavetronix SmartSensor, 2014, http://www.wavetronix.com.
  24. NTCIP Committee, The National Transportation Communications for ITS Protocol Online Resources, 2012, http://www.ntcip.org/.
  25. SmartMicro, Smart Microwave Sensors, 2012, http://www.smartmicro.de/.