Abstract: This paper presents a new method with adaptive inference capability for prediction of vehicle speed，considering the non-linearity and uncertainty of traffic flow.The first step is to cluster the traffic flow data and to build up the framework of the inference system.The second step is to train and optimize the coefficients with Adaptive Neuro-fuzzy Inference System（ANFIS）.The last step is to simulate and test the system with the MATLAB software.The testing result shows that the system is applicable.

摘要： 针对道路交通系统的非线性和随机性特点，设计一种具有学习能力的车速预测方法。首先，对交通流历史特征数据采用模糊聚类的方法进行状态分类并确立模型结构。然后，建立交通流状态预测的自适应神经模糊系统，以实测交通流数据进行系统参数优化训练。最后，利用MATLAB进行系统的仿真及检测。检测的预测结果表明系统具有良好的应用性能。