摘要
交通流量数据具有非周期性、非线性和随机性等特点.为了更准确地对未设置ETC路段交通流量进行预测,采取相应措施处理交通拥堵问题,提出了基于神经网络推论模型为主体的交通流量预测系统.通过实验验证了ARIMA乘积季节模型、BP神经网络和RBF神经网络的多种训练函数的预测精度及适应性.相对于常规预测方法,基于神经网络的预测方法具有更好的适应性,而且预测精度也更高.
Traffic flow data is characterized by non-periodicity, nonlinearity and randomness. In order to accurately predict the traffic flow without ETC, it take measures to solve the traffic jam problem quickly and accurately, a traffic flow prediction system is proposed based on a neural network inference model. It is verified the prediction accuracy and adaptability of the ARIMA seasonal model, BP neural networks and RBF neural networks with various training functions by experiments. Relative to conventional forecasting method, the prediction method which is based on neural network is more adaptability and prediction accuracy is higher than conventional forecasting method.
作者
蒲斌
李浩
卢晨阳
王治辉
刘华
PU Bin;LI Hao;LUChen-yang;WANGZhi-hui;LIU Hua(School of Software,Yunnan University,Ktmming 650500,China;Yunnan Science Research Institute of Communication & Transportation,Kunming 650011,China)
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2019年第1期53-60,共8页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金(61462095)
云南省软件工程重点实验室开放基金(2017SE204)
