降雨条件下城市快速路车速模糊神经网络预测方法

为了提高降雨条件下快速路车速短时预测的准确性，考虑到各影响因素的模糊性以及影响作用非线性变化特点，提出了一个以交通量、占有率和降雨量为输入，以车速为输出的模糊神经网络预测方法.利用上海市快速路的交通流与气象数据确定了最优模型结构，并与自回归积分滑动平均模型、反向传播神经网络模型和支持向量机模型进行对比分析.该方法的预测均方根误差为3.05 km?h-1，预测平均误差为3.95%，均优于其他3种方法.
A fuzzy neural network system was developed to improve urban expressway short term speed prediction accuracy on rainy days, taking fuzzy influencing factors such as traffic volume, occupancy and precipitation, as well as their non linear interaction into account. Based on the traffic flow and weather data of Shanghai, the best model structure was determined and its performance was evaluated against those of the existing autoregressive integrated moving average model, the back propagation neutral network, and the support vector machines model. The results show that the root mean square error and mean absolute percent error of the fuzzy neural network system are 3.05 km?h-1 and 3.95% respectively, which outperform those of the other three prediction models