Dynamic shortest path in stochastic traffic networks based on fluid neural network and Particle Swarm Optimization

The shortest path algorithm is critical for dynamic traffic assignment and for the realization of route guidance in intelligent transportation systems (ITS). In this paper, a hybrid Particle Swarm Optimization (PSO) algorithm combined fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO algorithm. Simulation experiments have been carried out on different traffic network topologies consisting of 15-70 nodes and the results showed that the proposed approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high success ratio for all the tested traffic networks. At the same time, the hybrid algorithms improve greatly the efficiency of the convergence of the fluid neuron network, and decrease the computation time of optimization path.must