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题名

Traffic Data Imputation with Ensemble Convolutional Autoencoder

作者
通讯作者Yu,James J.Q.
DOI
发表日期
2021-09-19
ISBN
978-1-7281-9143-0
会议录名称
卷号
2021-September
页码
1340-1345
会议日期
19-22 Sept. 2021
会议地点
Indianapolis, IN, USA
摘要
Intelligent transportation systems and related applications rely on high-quality traffic data. However, the data collected in real-world is often incomplete, which compromises the system performance. Traffic data imputation estimates the missing values by analyzing traffic flow features, therefore can improve the performance of related applications. Traditional imputation methods mainly focus on isolated traffic data sensors or road sections and show their limitations in representing complex spatial-temporal features. In this paper, we propose a novel ensemble model named ensemble convolutional auto encoder for the task. The observed values, together with the missing points are reconstructed into a two-dimensional matrix by the extracted spatial-temporal relation. Convolutional and deconvolutional layers are adopted to encode and decode spatial-temporal features, respectively. Besides, we train autoencoders with different input feature maps and ensemble the outputs by linear combination. Experimental results show that compared with other traffic data imputation methods, the proposed method can achieve better accuracy and has stable performance under various missing data scenarios with different types and rates.
关键词
学校署名
第一 ; 通讯
语种
英语
相关链接[Scopus记录]
收录类别
EI入藏号
20214511115126
EI主题词
Intelligent systems ; Learning systems
EI分类号
Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4
Scopus记录号
2-s2.0-85118452635
来源库
Scopus
全文链接https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9564839
引用统计
被引频次[WOS]:9
成果类型会议论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/255440
专题工学院_计算机科学与工程系
作者单位
Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China
第一作者单位计算机科学与工程系
通讯作者单位计算机科学与工程系
第一作者的第一单位计算机科学与工程系
推荐引用方式
GB/T 7714
Ye,Yongchao,Zhang,Shuyu,Yu,James J.Q.. Traffic Data Imputation with Ensemble Convolutional Autoencoder[C],2021:1340-1345.
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