计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 505-511.doi: 10.11896/JsJkx.190700045

• 数据库 & 大数据 & 数据科学 • 上一篇    下一篇

一种基于张量的车辆交通数据缺失估计新方法

张德干, 范洪瑞, 龚倡乐, 高瑾馨, 张婷, 赵彭真, 陈晨   

  1. 天津理工大学计算机科学与工程学院计算机视觉与系统教育部重点实验室 天津300384;
    天津理工大学计算机科学与工程学院智能计算及软件新技术天津市重点实验室 天津 300384
  • 发布日期:2020-07-07
  • 通讯作者: 高瑾馨(974281483@qq.com)
  • 基金资助:
    国家自然科学基金(61571328);天津市重大科技专项(15ZXDSGX00050,16ZXFWGX00010);天津市科技支撑重点项目(17YFZCGX00360);天津市自然科学基金重点项目(18JCZDJC96800); 天津市科技创新和131人才团队(TD12-5016,TD13-5025,No.2015-23)

New Method of Data Missing Estimation for Vehicle Traffic Based on Tensor

ZHANG De-gan, FAN Hong-rui, GONG Chang-le, GAO Jin-xin, ZHANG Ting, ZHAO Peng-zhen and CHEN Chen   

  1. Key Laboratory of Computer Vision and System,TianJin University of Technology,TianJin 300384,China
    TianJin Key Lab of Intelligent Computing& Novel software Technology,TianJin University of Technology,TianJin 300384,China
  • Published:2020-07-07
  • About author:ZHANG De-gan, born in 1969, Ph.D, professor, is a member of IEEE in 2001.His research interest includes ITS, WSN, IOT, etc.
    GAO Jin-xin, born in 1994, Ph.D candidate, is a member of IEEE in 2016.Her research interest includes WSN, industrial application, etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61571328),MaJor ProJect of Science and Technology in TianJin (15ZXDSGX00050,16ZXFWGX00010),TianJin Key ProJects supported by Science and Technology (17YFZCGX00360),TianJin Natural Science Foundation Key ProJect (18JCZDJC96800),Training Plan of TianJin Science and Technology Innovation and 131 Talent Team (TD12-5016,TD13-5025,2015-23) .

摘要: 面对当前庞大的智慧交通数据量,收集并统计处理是必要且重要的过程,但无法避免的数据缺失问题是目前的研究重点。文中针对车辆交通数据缺失问题提出一种基于张量的车辆交通数据缺失估计新方法:集成贝叶斯张量分解(Integrated Bayesian Tensor Decomposition,IBTD)。该算法在数据模型构建阶段,利用随机采样原理,将缺失数据随机抽取生成数据子集,并用优化后的贝叶斯张量分解算法进行插补。引入集成思想,将多个插补后的误差结果进行分析排序,考虑时空复杂度,择优平均得到最优结果。通过平均绝对百分比误差之后(Mean Absolute Percentage Error,MAPE)和均方根误差(Root Mean Square Error,RMSE)对提出模型的性能进行评估。实验结果表明,所提新方法能够有效地对不同缺失量的交通数据集进行插补,并能得到很好的插补结果。

关键词: 贝叶斯张量分解, 交通数据, 数据缺失, 随机采样, 张量

Abstract: In the face of the current huge amount of intelligent traffic data,collecting and statistical processing is a necessary and important process,but the problem of inevitable data missing is the current research focus.Aiming at the problem of vehicle traffic data missing,this paper proposed a new method based on tensor for vehicle traffic data missing estimation,Integrated Bayesian tensor decomposition (IBTD).In the data model construction stage,the random sampling principle was used to randomly extract the missing data to generate a subset of data,and the optimized Bayesian tensor decomposition algorithm was used for interpolation.By introducing the integration idea,the error results after multiple interpolations were analyzed and sorted,consider the spatio-temporal complexity,and choose the optimal average to get the best result.The performance of the proposed model was evalua-ted by mean absolute percentage error (MAPE) and root mean square error (RMSE).Experimental results show that the proposed method can effectively interpolate the traffic datasets with different missing quantities and get good interpolation results.

Key words: Bayesian tensor decomposition, Data missing, Random sampling, Tensor, Traffic data

中图分类号: 

  • TP399
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