基于自适应秩动态张量分析的短时交通流预测

doi:10.11959/j.issn.2096-3750.2019.00116

物联网学报 ›› 2019, Vol. 3 ›› Issue (3): 18-25.doi: 10.11959/j.issn.2096-3750.2019.00116

基于自适应秩动态张量分析的短时交通流预测

何领朝,林东,冯心欣()

福州大学物理与信息工程学院，福建福州 350108

修回日期:2019-04-18 出版日期:2019-09-30 发布日期:2019-10-14
作者简介:何领朝（1991- ），男，河南林州人，福州大学物理与信息工程学院通信与信息系统专业硕士生，主要研究方向为车联网数据采集与分析和张量数据分析。|林东（1969- ），男，福建福州人，博士，福州大学物理与信息工程学院通信工程系副教授，主要研究方向为视频信号处理、移动通信和信息安全。|冯心欣（1983- ），女，福建福州人，福州大学物理与信息工程学院通信工程系副教授，主要研究方向为经济学理论及其在通信网络中的应用、机器学习理论及其在数据处理中的应用。
基金资助:
国家自然科学基金资助项目(61601126);国家自然科学基金资助项目(61571129)

Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis

Lingchao HE,Dong LIN,Xinxin FENG()

School of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China

Revised:2019-04-18 Online:2019-09-30 Published:2019-10-14
Supported by:
The National Natural Science Foundation of China(61601126);The National Natural Science Foundation of China(61571129)

摘要/Abstract

摘要：

在智能交通系统中，短时交通流预测可以为路线规划、交通管理和公共安全等领域提供数据支撑。为了提高数据缺失和异常情况下的预测准确性，提出了一种基于自适应秩动态张量分析的算法来进行短时交通流预测。首先构造了覆盖周、天、时间窗口和空间4个维度的张量，以挖掘交通流数据之间的多模相关性。其次，利用滑动窗口模型，形成动态结构的张量流数据。然后将主成分分析算法扩展成可以接收张量输入的离线张量分析算法，并引入自适应秩和遗忘因子形成自适应秩动态张量分析算法。最后将张量流数据输入自适应秩动态张量分析算法中，实现对短时交通流数据的预测。实验结果显示，即使在数据有缺失的情况下，自适应秩动态张量分析算法也能实现良好的预测。

关键词: 短时交通流预测, 数据缺失, 动态张量分析, 多模信息

Abstract:

Short-term traffic flow prediction in intelligent transportation system can provide data support in areas such as route planning,traffic management,public safety and so on.In order to improve the prediction accuracy with missing and abnormal data,a short-term traffic flow prediction method based on the adaptive rank dynamic tensor analysis was proposed.Firstly,a four dimensional tensor consisted of week,day,time and space was constructed,which could excavate the multimodal correlation of traffic flow data.Secondly,tensor flow data with dynamic structure was formed by using sliding window model.The principal component analysis (PCA) algorithm was extended to an offline tensor analysis algorithm that could accept tensor input.Then the adaptive rank and the forgetting factor were introduced to generate an adaptive rank dynamic tensor analysis algorithm.Finally,the tensor stream data was inputted into the adaptive rank dynamic tensor analysis algorithm to realize the short-term traffic flow prediction.The experimental results show that a good prediction can be achieved even with data missing.

Key words: short-term traffic flow prediction, data missing, dynamic tensor analysis, multimodal information

中图分类号:

TN915

何领朝,林东,冯心欣. 基于自适应秩动态张量分析的短时交通流预测[J]. 物联网学报, 2019, 3(3): 18-25.

Lingchao HE,Dong LIN,Xinxin FENG. Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis[J]. Chinese Journal on Internet of Things, 2019, 3(3): 18-25.

图/表 8

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算法	MAPE	MAE
四阶自适应秩动态张量分析	0.082	23.25
四阶动态张量分析	0.084	26.79
三阶动态张量分析	0.175	63.01
HaLRTC	0.269	90.78
Tucker-als	0.298	103.2

基于自适应秩动态张量分析的短时交通流预测

Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis

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