Advertisement

Q Value-Based Dynamic Programming with Boltzmann Distribution by Using Neural Network

  • Wenxin Yu
  • Liang Yu
  • Gang He
  • Yibo Fan
  • Gang He
  • Jiu Xu
  • Zhuo Yang
  • Zhiqiang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

Abstract

In this paper, a feedback method using neural network is proposed with Q Value-based Dynamic Programming based on Boltzmann Distribution for static road network. The neural network can supply more distribute strategies and the feedback method chooses the best result from the strategies produced by neural network. The method distributes vehicles well on all the optimal routes from the origin to destination according to the gradual decreasing parameters, which are used in the neural network. This method can overcome local optimum problems to some extent by setting appropriate parameters at the beginning. The proposed method is evaluated by using the Kitakyushu city (Fukuoka, Japan) road network data. The simulation result shows that the better result can be obtained than conventional QDPBD method by training parameters.

Keywords

Q value Boltzmann Distribution Optimal routes Feedback Neural network Vehicle distribution 

Notes

Acknowledgments

This research was supported by A Project【16ZA0131 】which supported by Scientific Research Fund of Sichuan Provincial Education Department,【2018GZ0517】which supported by Sichuan Provincial Science and Technology Department, 【2018KF003】 Supported by State Key Laboratory of ASIC & System, Science and Technology Planning Project of Guangdong Province 【2017B010110007】, the National Natural Science Foundation of China grants【61672438】.

References

  1. 1.
    Velaga, N.R., Quddus, M.A., Bristow, A.L., Zheng, Y.: Map-aided integrity monitoring of a land vehicle navigation system. IEEE Trans. Intell. Transp. Syst. 13(2), 848–858 (2012)CrossRefGoogle Scholar
  2. 2.
    Yang, J., Chou, L., Chang, Y.: Electric-vehicle navigation system based on power consumption. IEEE Trans. Veh. Technol. 65(8), 5930–5943 (2015)CrossRefGoogle Scholar
  3. 3.
    Dijkstra, E.: A note on two problems in connection with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, J., Feng, Y., Shi, F., Wang, G., Ma, B., Li, R., Jia, X.: Vehicle routing in urban areas based on the oil consumption weight –Dijkstra algorithm. Intell. Transp. Syst. 10(7), 495–502 (2016)CrossRefGoogle Scholar
  5. 5.
    Jagadeesh, G.R., Srikanthan, T., Quek, K.H.: Heuristic techniques for accelerating hierarchical routing on road networks. IEEE Trans. Intell. Transp. Syst. 3(4), 301–309 (2002)CrossRefGoogle Scholar
  6. 6.
    Mainali, M.K., Shimada, K., Mabu, S., Hirasawa, K.: Optimal route based on dynamic programming for road networks. J. Adv. Comput. Intell. Intell. Inf. 12(6), 546–553 (2008)CrossRefGoogle Scholar
  7. 7.
    Guggenheim, E.A., Green, M.S.: Boltzmann’s distribution law. Phys. Today 9(8), 34–36 (1955)CrossRefGoogle Scholar
  8. 8.
    Yu, S., Xu, Y., Mabu, S., Mainali, M.K., Shimada, K., Hirasawa, K.: Q value-based dynamic programming with Boltzmann distribution in large scale road network. Sice JCMSI 4(2), 129–136 (2011)CrossRefGoogle Scholar
  9. 9.
    Halpin, S.M., Burch, R.F.: Applicability of neural networks to industrial and commercial power systems: a tutorial overview. IEEE Trans. Ind. Appl. 33(5), 1355–1361 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Southwest University of Science and TechnologyMianyangChina
  2. 2.State Key Laboratory of ASIC & SystemShanghaiChina
  3. 3.Xidian UniversityXi’anChina
  4. 4.Rakuten Institute of TechnologyBostonUSA
  5. 5.Guangdong University of TechnologyGuangzhouChina
  6. 6.Sichuan Civil-Military Integration InstituteMianyangChina

Personalised recommendations