摘要
在智能交通系统的诱导、控制和管理中,实时准确的短时交通流量预测具有重要意义。为提高预测精度,充分分析交通流特性和外部空间关联对预测结果的影响,提出一种短时交通流预测模型——SARIMA-RF模型。利用SARIMA模型良好的线性拟合能力,提取交通流数据中的周期性特征;利用随机森林模型较强的泛化能力,分析交通流的时空相关性,得出预测结果。实验结果表明,该组合模型与单一模型相比具有更高的预测精度,是一种有效的预测方法。
Real-time short-term traffic flow forecast is of great significance in the induction,control and management of intelligent transportation system(ITS).To improve prediction accuracy and fully analyze the impact of traffic flow characteristics and external spatial correlation on prediction results,a short-term traffic flow prediction model,namely SARIMA-RF model was proposed.The periodic features in traffic flow data were extracted using the good linear fitting ability of SARIMA model.The spatio-temporal correlation of traffic flow was analyzed and the forecast result was obtained using the strong generalization ability of stochastic forest model.Experimental results show that the combined model has higher prediction accuracy than single model and it is an effective prediction method.
作者
熊亭
戚湧
张伟斌
李千目
XIONG Ting;QI Yong;ZHANG Wei-bin;LI Qian-mu(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《计算机工程与设计》
北大核心
2019年第2期501-507,共7页
Computer Engineering and Design
基金
国家重点研发计划政府间国际科技创新合作重点专项基金项目(2016YFE0108000)
江苏省重点研发计划基金项目(BE2017163)
江苏省普通高校研究生实践创新计划基金项目(SJCX17_0108)
关键词
交通流量预测
时空相关性
随机森林
SARIMA模型
组合预测模型
traffic flow forecast
spatio-temporal correlation
random forest model
SARIMA model
combined forecasting model