摘要
针对短时交通流数据具有非线性、不确定性等特点,提出一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和随机森林(random forest,RF)的组合预测模型。首先,利用EEMD算法将原始交通流数据的区间平均速度序列分解为若干个本征模函数(intrinsic mode function,IMF)和一个残差分量(residual,RES),提取出交通流数据在不同时频的信息;其次,将第一个分量进行二次EEMD分解,细化交通流的随机信息;再次,将分解得到的各个分量分别使用RF进行预测,构建子模型;最后,将所有子模型的预测值线性求和,得到最终的预测结果。采用阿拉尔市某路段的实际交通流数据进行实验,结果表明,EEMD和RF的组合预测模型优于单一的RF模型,并且对IMF1进行二次EEMD分解可进一步提高组合预测模型的准确率。
In view of the nonlinear and uncertainty characteristics of short-term traffic flow data,a short-term traffic flow prediction model based on ensemble empirical mode decomposition(EEMD) and random forest(RF) was proposed.Firstly,the space mean speed sequences of the original traffic flow data was decomposed into several intrinsic mode functions(IMF) and a residual component(RES) by using the EEMD algorithm,which extracted the information of traffic flow data at different time frequencies.Secondly,the first component was decomposed by EEMD to refine the random information of traffic flow data.Thirdly,each component after decomposition was predicted by using RF,which can obtain several submodels.Finally,the prediction values of all submodels were summed linearly to obtain the final prediction results.The actual traffic flow data of a certain section road in Alar City was used for experiments.The results show that the prediction performance of the EEMD and RF combined model is better than that of the single RF model,and the secondary decomposition of high-frequency sequence can further improve the accuracy of the combined model.
作者
田佳
王德勇
师文喜
TIAN Jia;WANG De-yong;SHI Wen-xi(School of Information Science and Engineering,Xinjiang University,Urumqi 830017,China;Xinjiang Lianhaichuangzhi Information Technology Co.,Ltd.,Urumqi 830011,China;China Academy of Electronics and Information Technology,Beijing 100041,China)
出处
《科学技术与工程》
北大核心
2023年第29期12612-12619,共8页
Science Technology and Engineering
基金
国家自然科学基金(U20B2060)。
关键词
智能交通
短时交通流预测
集合经验模态分解
随机森林
intelligent transportation
short-term traffic flow prediction
ensemble empirical mode decomposition
random forest
