基于LVQ神经网络的城市快速路事件自动检测算法

Automatic incident detection algorithm based on LVQ neural network for urban expressway

基于具有自组织功能的学习矢量量化(LVQ)神经网络设计了城市快速路异常事件的自动检测算法,提出分车道检测的构想。研究了原始数据筛选、输入向量模式、神经元个数及检测时段等参数的选择。基于小波分析技术对原始数据的高频噪声进行滤波,引入长车流量作为输入参数,并对比了引入前后的检测效果。选用加利福尼亚算法作为评价的参考依据,对其执行过程和门限值的选择进行了研究。

An algorithm based on the learning vector quantization（LVQ）neural network with self-organizing feature maps was developed to detect the urban expressway incidents.A concept of detection based on each lane separately was proposed.The input data screening,the input vector mode,the quantity of neural element,and the detection time interval selection were investigated.The high-frequency noise in the input data was filtered using the db3 wavelet denoising technique.The long vehicle flow volume was introduced into the input vector,and the effect of its introduction was studied comparatively.The implement process of the proposed algorithm and the selection the thresholds were studied using the traditional California algorithm as reference of estimation.