Dynamic Shortest Path Algorithm in Stochastic Traffic Networks Using PSO Based on Fluid Neural Network
Yanfang Deng, Hengqing Tong
DOI: 10.4236/jilsa.2011.31002   PDF    HTML     8,941 Downloads   18,209 Views   Citations


The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.

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Y. Deng and H. Tong, "Dynamic Shortest Path Algorithm in Stochastic Traffic Networks Using PSO Based on Fluid Neural Network," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 1, 2011, pp. 11-16. doi: 10.4236/jilsa.2011.31002.

Conflicts of Interest

The authors declare no conflicts of interest.


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