高贺,,
魏帅康,
孙双晨,
何怡刚
合肥工业大学电气与自动化工程学院 合肥 230009
基金项目:国家自然科学基金(51577046, 61673153)，教育部科学技术研究重大项目(313018)，安徽省科技计划重点项目(1301022036)
详细信息
作者简介:殷礼胜：男，1974年生，博士，副教授，研究方向为复杂系统建模、非线性时间序列预测、交通流预测等
高贺：男，1993年生，硕士生，研究方向为交通流预测、智能控制系统
魏帅康：男，1995年生，硕士生，研究方向为交通流预测、复杂系统建模
孙双晨：男，1995年生，硕士生，研究方向为交通流预测、智能控制系统
何怡刚：男，1966年生，博士，教授，研究方向为通信信道建模与检测、复杂电磁分析与建模等
通讯作者:高贺 gaohe1104@163.com
中图分类号:U491.1计量
文章访问数:466
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被引次数:0
出版历程
收稿日期:2020-01-05
修回日期:2020-08-22
网络出版日期:2020-09-17
刊出日期:2021-04-20
Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time
Lisheng YIN,He GAO,,
Shuaikang WEI,
Shuangchen SUN,
Yigang HE
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Funds:The National Natural Science Foundation of China (51577046, 61673153), The Key Grant Project of Chinese Ministry of Education (313018), Anhui Provincial Science and Technology Foundation of China (1301022036)
摘要
摘要:针对城市短时交通流序列非线性和混沌性的特点，为提高短时交通流的预测精度，该文提出一种基于多维时空的非线性主成分分析(NPCA)和相空间重构(PSR)的改进灰色(IGM(1,1))组合预测模型。首先，使用数据相关性的非线性主成分分析算法对多维交通流量序列进行时空降维，同时保留影响预测点的主要交通流量数据，从而提高建模的精确度；其次，利用多维时空交通流量序列相空间重构放大交通流量内部的细微特征，以使其内在规律得以充分展现，进一步提升预测精度；最后，结合背景值改进的灰色模型适应于线性、非线性以及所需数据少的特点，进行短时交通流预测。实验结果表明，NPCA-PSR-IGM(1,1)组合预测模型的平均相对误差相比NPCA-PSR-GM(1,1)组合预测模型减小3.12%，其标准偏差相对PCA-PSR-IGM(1,1)组合预测模型从15.7091下降到2.0589。同时与最新的预测模型相比，该组合预测模型也提高了预测精度，达到了较好的预测效果。
关键词:短时交通流预测/
多维时空/
非线性主成分分析/
相空间重构/
改进灰色模型
Abstract:In view of the nonlinear and chaos of urban short-term traffic flow sequence, this article proposes a combined prediction model based on multi-dimensional spatio-temporal Nonlinear Principal Component Analysis (NPCA) and Phase Space Reconstructed (PSR) Improved Gray Model (IGM(1,1)) in order to improve its forecast accuracy. First, the data correlation NPCA algorithm is used to reduce the spatial and temporal dimensions of multi-dimensional traffic flow sequences, while preserving the main traffic flow data that affects the predicted points, so as to improve the accuracy of the modeling. Phase space reconstruction amplifies the subtle features inside the traffic flow, so that its internal laws can be fully displayed, and improve further the prediction accuracy. Finally, the gray model combined with the improved background value is adapted to the characteristics of linearity, non-linearity and less required data. Short-term traffic flow is predicted. The experimental results show that the average relative error of the NPCA-PSR-IGM (1,1) combination prediction model is 3.12% smaller than that of the NPCA-PSR-GM (1,1) combination prediction model, and its standard deviation is relative to the PCA-PSR-IGM (1,1) combination prediction model has dropped from 15.7091 to 2.0589. At the same time, compared with the latest prediction model, the combined prediction model also improves the prediction accuracy and achieves a better prediction effect.
Key words:Short-term traffic flow prediction/
Multidimensional space-time/
Nonlinear Principal Component Analysis (NPCA)/
Phase Space Reconstruction (PSR)/
Improved Gray Model (IGM(1,1))
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https://jeit.ac.cn/article/exportPdf?id=1e1f4b41-958b-47d4-9fe4-d802db304ad7
