基于小波降噪与贝叶斯神经网络联合模型的短时交通流量预测

在短时间内准确、稳定地预测出交通流量,是实现智能交通控制系统的重要环节,对于交叉口信号控制方案的实时调整具有重要意义。鉴于此,提出一种 联合模型的预测方式,引入 和 作为模型评价指标,从精度和稳定性两个方面对模型进行评价。结果表明,在 、 、 等不同的时间预测尺度下, 联合模型的 和 均小于小波网络、贝叶斯网络、 网络等方法,短时交通流量预测结果的精度和稳定性得到了不同程度的提高。

Short-term traffic flow prediction based on Wavelet Denoising and Bayesian Neural Network Model

Accurate and stable prediction of traffic flow in a short period of time is an important link in the realization of intelligent traffic control system, which is of great significance for real-time adjustment of intersection signal control scheme. In view of this, a prediction method of WD-BNN joint model is proposed, and MAPE and RMSE are introduced as evaluation indexes to evaluate the model from two aspects of accuracy and stability. The results show that MAPE and RMSE of WD-BNN combined model are smaller than Wavelet network, Bayesian network, l-m network and other methods under different time prediction scales of 5min, 10min, 15min, etc., and the accuracy and stability of results have been improved to varying degrees.