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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
Yang, J., Chou, L., Chang, Y.: Electric-vehicle navigation system based on power consumption. IEEE Trans. Veh. Technol. 65(8), 5930–5943 (2015)
Dijkstra, E.: A note on two problems in connection with graphs. Numer. Math. 1(1), 269–271 (1959)
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)
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)
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)
Guggenheim, E.A., Green, M.S.: Boltzmann’s distribution law. Phys. Today 9(8), 34–36 (1955)
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)
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)
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】.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, W. et al. (2018). Q Value-Based Dynamic Programming with Boltzmann Distribution by Using Neural Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-04239-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04238-7
Online ISBN: 978-3-030-04239-4
eBook Packages: Computer ScienceComputer Science (R0)