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

doi:10.11959/j.issn.2096-3750.2019.00116

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基于自适应秩动态张量分析的短时交通流预测

理论与技术 | 更新时间：2024-06-05

- 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
- 物联网学报 2019年3卷第3期页码：18-25
- 作者机构：
- 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。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61601126);国家自然科学基金资助项目;The National Natural Science Foundation of China(61571129)
- DOI：10.11959/j.issn.2096-3750.2019.00116
  中图分类号： TN915
- 纸质出版日期：2019-09-30，
  
  网络出版日期：2019-09，
- 稿件说明：
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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.
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. DOI： 10.11959/j.issn.2096-3750.2019.00116.

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. DOI： 10.11959/j.issn.2096-3750.2019.00116.

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。

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.

关键词

短时交通流预测数据缺失动态张量分析多模信息

Keywords

short-term traffic flow predictiondata missingdynamic tensor analysismultimodal information

references

张彦, 张科, 曹佳钰 ,等. 边缘智能驱动的车联网[J]. 物联网学报, 2018,2(4): 40-48.

ZHANG Y, ZHANG K, CAO J Y ,et al. Internet of vehicles empowered by edge intelligence[J]. Chinese Journal on Internet of Things, 2018,2(4): 40-48.

余辰, 张丽娟, 金海 . 大数据驱动的智能交通系统研究进展与趋势[J]. 物联网学报, 2018,2(1): 56-63.

YU C, ZHANG L J, JIN H . Research progress and trend of big data-driven intelligent transportation system[J]. Chinese Journal on Internet of Things, 2018,2(1): 56-63.

熊建芳 . 浅谈物联网在智能交通中的应用[J]. 智能计算机与应用, 2018,8(6): 177-179.

XIONG J F . The application of the Internet of things in the intelligent transportation[J]. Intelligent Computer and Applications, 2018,8(6): 177-179.

ZHANG J, WANG F Y, WANG K ,et al. Data-driven intelligent transportation systems:a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2011,12(4): 1624-1639.

RAN B, JIN P J, BOYCE D ,et al. Perspectives on future transportation research:impact of intelligent transportation system technologies on next generation transportation modeling[J]. Journal of Intelligent Transportation Systems, 2012,16(4): 226-242.

CHEN Y, HU J, ZHANG Y ,et al. Traffic data analysis using kernel PCA and self-organizing map[C]// Intelligent Vehicles Symposium. IEEE, 2006.

QU L, ZHANG Y, HU J ,et al. A BPCA based missing value imputing method for traffic flow volume data[C]// Intelligent Vehicles Symposium. IEEE, 2008.

ZHANG Y, LIU Y . Missing traffic flow data prediction using least squares support vector machines in urban arterial streets[C]// 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2009: 76-83.

TAN H, FENG G, FENG J ,et al. A tensor-based method for missing traffic data completion[J]. Transportation Research Part C:Emerging Technologies, 2013,28: 15-27.

LIU J, MUSIALSKI P, WONKA P ,et al. Tensor completion for estimating missing values in visual data[J]. IEEE transactions on pattern analysis and machine intelligence, 2012,35(1): 208-220.

TOMIOKA R, HAYASHI K, KASHIMA H . On the extension of trace norm to tensors[C]// NIPS Workshop on Tensors,Kernels,and Machine Learning. 2010:7.

SIGNORETTO M, DE LATHAUWER L, SUYKENS J A K . Nuclear norms for tensors and their use for convex multilinear estimation[J].,2010:43. Submitted to Linear Algebra and Its Applications, 2010:43.

SIGNORETTO M, DINH Q T, DE LATHAUWER L ,et al. Learning with tensors:a framework based on convex optimization and spectral regularization[J]. Machine Learning, 2014,94(3): 303-351.

SHAN H, BANERJEE A, NATARAJAN R . Probabilistic tensor factorization for tensor completion[J]. Department of Computer Science and Engineering , 2011: 1-4.

ACAR E, DUNLAVY D M, KOLDA T G ,et al. Scalable tensor factorizations for incomplete data[J]. Chemometrics and Intelligent Laboratory Systems, 2011,106(1): 41-56.

SUN J, TAO D, FALOUTSOS C . Beyond streams and graphs:dynamic tensor analysis[C]// Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006: 374-383.

HAO F, JIAO M, MIN G ,et al. A trajectory-based recruitment strategy of social sensors for participatory sensing[J]. IEEE Communications Magazine, 2014,52(12): 41-47.

TAN H, WU Y, SHEN B ,et al. Short-term traffic prediction based on dynamic tensor completion[J]. IEEE Transactions on Intelligent Transportation Systems, 2016,17(8): 2123-2133.

DE LATHAUWER L, DE MOOR B, VANDEWALLE J . A multilinear singular value decomposition[J]. SIAM Journal on Matrix Analysis and Applications, 2000,21(4): 1253-1278.

MIN W, WYNTER L . Real-time road traffic prediction with spatio-temporal correlations[J]. Transportation Research Part C:Emerging Technologies, 2011,19(4): 606-616.

BRIN S, PAGE L . The anatomy of a large-scale hypertextual web search engine[J]. Computer Networks and ISDN Systems, 1998,30(1-7): 107-117.

LIU J, MUSIALSKI P, WONKA P ,et al. Tensor completion for estimating missing values in visual data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,35(1): 208-220.

CHEN X, HE Z, WANG J . Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition[J]. Transportation Research Part C:Emerging Technologies, 2018,86: 59-77.

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