Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 146070, 10 pages
http://dx.doi.org/10.1155/2015/146070
Research Article

A Cooperative -Learning Path Planning Algorithm for Origin-Destination Pairs in Urban Road Networks

School of Information Science and Engineering, Central South University, 22 South Shaoshan Road, Changsha 410075, China

Received 25 May 2015; Accepted 21 September 2015

Academic Editor: Chronis Stamatiadis

Copyright © 2015 Xiaoyong Zhang 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. H. Shimoura and K. Tenmoku, “Development of elemental algorithms for future dynamic route guidance system,” in Proceedings of the Vehicle Navigation and Information Systems Conference (VNIS '94), pp. 321–326, Yokohama, Japan, 1994. View at Publisher · View at Google Scholar
  2. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968. View at Publisher · View at Google Scholar
  4. C.-K. Lee, “A multiple-path routing strategy for vehicle route guidance systems,” Transportation Research C: Emerging Technologies, vol. 2, no. 3, pp. 185–195, 1994. View at Publisher · View at Google Scholar · View at Scopus
  5. D. Eppstein, “Finding the k shortest paths,” SIAM Journal on Computing, vol. 28, no. 2, pp. 652–673, 1999. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. V. M. Jiménez and A. Marzal, “Computing the k shortest paths: a new algorithm and an experimental comparison,” in Algorithm Engineering, vol. 1668 of Lecture Notes in Computer Science, pp. 15–29, 1999. View at Publisher · View at Google Scholar
  7. E. A. Dinic, “Algorithm for solution of a problem of maximum flow in a network with power estimation,” Soviet Math, vol. 11, no. 5, pp. 1277–1280, 1970. View at Google Scholar
  8. D. Torrieri, “Algorithms for finding an optimal set of short disjoint paths in a communication network,” IEEE Transactions on Communications, vol. 40, no. 11, pp. 1698–1702, 1992. View at Publisher · View at Google Scholar
  9. R. S. Sutton and A. G. Barto, “Reinforcement learning: an introduction,” IEEE Transactions on Neural Networks, vol. 9, no. 5, p. 1054, 1998. View at Publisher · View at Google Scholar
  10. M. Z. Arokhlo, “Route guidance system using multi-agent reinforcement learning,” in Proceedings of the 7th International Conference on Information Technology in Asia (CITA '11), pp. 1–5, Kuching, Malaysia, July 2011.
  11. M. Zolfpour-Arokhlo, A. Selamat, and S. Z. M. Hashim, “Self-adaptive and multi-agent reinforcement learning in route guidance system,” in Proceedings of the 5th Malaysian Conference in Software Engineering (MySEC '11), pp. 383–387, IEEE, Johor Bahru, Malaysia, December 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Zhang and J.-M. Xu, “A dynamic route guidance arithmetic based on reinforcement learning,” in Proceeding of the 4th International Conference on Machine Learning and Cybernetics, pp. 3607–3611, August 2005. View at Scopus
  13. C. Wu, O. Satoshi, S. Ohzahata, and T. Kato, “Flexible, portable, and practicable solution for routing in VANETs: a fuzzy constraint Q-learning approach,” IEEE Transactions on Vehicular Technology, vol. 62, no. 9, pp. 4251–4263, 2013. View at Publisher · View at Google Scholar
  14. K. Y. Chan and T. S. Dillon, “On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 1, pp. 50–59, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Li, S. G. Anavatti, and T. Ray, “Short-term traffic flow prediction using different techniques,” in Proceedings of the 37th Annual Conference of the IEEE Industrial Electronics Society (IECON '11), pp. 2423–2428, Melbourne, Australia, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. B. R. Hellinga and L. Fu, “Reducing bias in probe-based arterial link travel time estimates,” Transportation Research, Part C: Emerging Technologies, vol. 10, no. 4, pp. 257–273, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Tomio, S. Takaaki, and M. Taka, “Route identification and travel time prediction using probe-car data,” International Journal of ITS Research, vol. 2, no. 1, pp. 21–28, 2004. View at Google Scholar
  18. Y. Y. Chen, M. G. H. Bell, and K. Bogenberger, “Reliable pretrip multipath planning and dynamic adaptation for a centralized road navigation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 1, pp. 14–19, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. P. Jindahra and K. Choocharukul, “Short-run route diversion: an empirical investigation into variable message sign design and policy experiments,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 388–397, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Xing and X. Zhou, “Reformulation and solution algorithms for absolute and percentile robust shortest path problems,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 943–954, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. K. Y. Chan, S. Khadem, T. S. Dillon, V. Palade, J. Singh, and E. Chang, “Selection of significant on-road sensor data for short-term traffic flow forecasting using the Taguchi method,” IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp. 255–266, 2012. View at Publisher · View at Google Scholar