基于ANFIS混合模型的短时交通流预测

城市短时交通流预测可以帮助人们选择出行最优路线，提高出行效率，其研究在交通拥堵日益严重的今天十分必要.受天气等多种因素影响，短时交通流的精确预测比较困难，为改善短时交通流预测的精度，本文提出了一种基于自适应模糊推理系统（ANFIS）的混合模型.该混合模型用周期性知识模型及残差数据驱动ANFIS模型集成得到.为验证所提出的混合模型的性能，与倒向传播神经网络（BPNN）模型和普通ANFIS模型进行对比.实验结果证明混合模型在交通流预测方面有更好的适用性和准确度.

Short-Term Traffic Flow Prediction Based on ANFIS Hybrid Model

Urban short-term traffic flow forecasting can help people choose the optimal route for travel and improve travel efficiency, which is necessary because the traffic congestion increasingly serious today. It is difficult to predict short-term traffic flow accurately because there are various factors can influence short-term traffic flow such as weather. To improve the accuracy of short-term traffic flow prediction, this study proposes a hybrid model based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The hybrid model is combined with the periodicity knowledge model and the ANFIS model which has been driven by residual data. To verify the performance of the proposed hybrid model, it is compared with the Backward Propagating Neural Network (BPNN) model and the normal ANFIS model. The experimental results show that the hybrid model has better applicability and accuracy in traffic flow prediction.