快速路截面数据和车牌识别数据融合算法

为了提高快速路交通流检测精度,在对快速路截面数据和车牌识别数据预处理方法研究的基础上,提出了基于遗传算法优化的BP神经网络数据融合算法,并以VISSIM模拟交通流数据为对象,通过MATLAB程序实现该算法的仿真验证,同时与传统BP神经网络融合算法进行对比分析.结果表明,该算法融合的平均相对误差为0.73%,传统BP神经网络融合的平均相对误差为1.55%,融合精度显著提高.

Fusion Algorithm for Section Detector Data and License Plate Recognition Data of Expressway

In order to improve the detection precision of expressway traffic flow,based on the study of pretreatment methods for section detector data and license plate recognition data,a BP neural network method optimized by genetic algorithm was proposed to fuse the two detected datas.After that,the fusion algorithm was verified by MATLAB through the VISSIM simulation of traffic data of expressway,and then the result was compared with traditional BP algorithm.The result shows that the average relative error of the optimized BP neural network algorithm is 0.73%,and it improves the fusion precision significantly compared with the traditional BP neural network that has an average relative error of 1.55%.