关键词: 智能交通系统（ITS）, 萤火虫优化算法（GSO）, T-S模型, 模糊神经网络, 交通流量, 预测

Abstract: Based on the glowworm swarm optimization (GSO) and T-S fuzzy neural network (TSFNN), this paper proposes a prediction algorithm for traffic flow of T-S fuzzy neural network optimized glowworm swarm optimization (GSOTSFNN). The proposed algorithm uses GSO to get the optimal parameter configuration, thus can perform mapping ability of T-S fuzzy neural network for generalization. The efficiency of the proposed prediction algorithm is tested by the simulation of real traffic flow. The simulation results show that the proposed algorithm has higher forecasting accuracy compared with the traditional T-S fuzzy neural network and T-S fuzzy neural network optimized genetic algorithm, so it is feasible and effective in the practical prediction of traffic flow.

Key words: intelligent transportation system (ITS), glowworm swarm optimization (GSO), T-S model, fuzzy neural network, traffic flow, prediction