基于神经网络算法的交通流量预测建模与计算

Traffic flow prediction modeling and calculation based on neural network algorithm

为了提高交通流量预测数据的准确度,文中利用神经网络算法提出一种短时交通流量的预测模型。通过分析交通流量的概念和特征,设计相应的预测评价体系,使用拉格朗日中值定理与小波变换,实现交通流量数据的插值、降噪和归一化。基于改进的神经网络算法,建立和优化相应的预测数学模型。在评价体系的基础上,完成预测结果的计算与评估。仿真测试结果表明,改进神经网络算法的应用有效降低了预测结果的误差,提高了交通流量预测模型计算的准确度。

A short-term traffic flow prediction model is proposed by means of the neural network algorithm to improve the accuracy of traffic flow prediction data. The corresponding prediction and evaluation system is designed by analyzing the concept and feature of traffic flow,and the interpolation,noise reduction and normalization of traffic flow data are realized by means of the Lagrange mean value theorem and wavelet transform. On the basis of the improved neural network algorithm, the corresponding prediction mathematical model is established and optimized. The calculation and evaluation of the prediction results are completed on the basis of the evaluation system. The simulation testing results show that the improved neural network algorithm can reduce the error of the prediction results effectively,and increase the calculation accuracy of the traffic flow prediction model.