移动群智感知下的交通流速缺失数据恢复算法  

Crowdsensing Based Traffic Velocity Missing Data Recovery Algorithm

作　　者：张建宗 陶丹 ZHANG Jian-zong;TAO Dan(Institute of Signal Processing and Artificial Intelligent,School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学电子信息工程学院信息处理与人工智能研究所,北京100044

出　　处：《小型微型计算机系统》2021年第2期225-230,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金“面上”项目(61872027)资助.

摘　　要：GPS等设备的普及使得基于大规模车辆数据的城市级道路状态评估成为可能,本文研究移动群智感知下的交通流速缺失数据恢复问题.首先,利用探测车收集车辆数据,设计了基于网格密度提取路网的方法;其次,针对GPS数据特点设计一种自适应的路段流速计算方法,得到交通流速矩阵;再次,对交通状况评估时存在的数据缺失情形进行分类,基于数据时空特征改进了压缩感知的稀疏基,有效地将交通流速缺失数据的预测问题建模成稀疏向量的恢复问题;最后,基于大规模真实出租车数据集对所提出算法性能进行全面验证.结果表明:在数据缺失程度大于50%时,本文所提出算法能够准确地恢复缺失数据,且性能优于其他同类算法.The popularization of GPS makes it possible to evaluate the condition of urban roads based on large-scale vehicle data.In this paper,we study the problem of traffic velocity missing data recovery based on crowdsensing.Firstly,vehicle data is collected by probe vehicles,and a grid density based road network extraction method is designed.Secondly,according to the characteristics of GPS data,a self-adaptive road segment velocity calculation method is designed in order to obtain the traffic velocity matrix.Thirdly,we classify the missing data in traffic condition evaluation,and propose an improved sparse representation on basis of compressed sensing by considering the spatio-temporal correlation.In this way,the problem of traffic velocity missing data recovery can be modeled effectively as a sparse vector recovery one.Finally,based on a large-scale dataset,we verify the effectiveness of the proposed algorithm.The experimental results show that the proposed algorithm can accurately recover the missing data when the level of data missing is greater than 50%,and its performance is better than those of other similar algorithms.

关 键 词：群智感知 数据恢复 路网提取 交通流速 压缩感知 时空相关性 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]