An efficient realization of deep learning for traffic data imputation
Duan, Yanjie; Lv, Yisheng; Liu, Yu-Liang; Wang, Fei-Yue
2016-11-01
发表期刊TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷号72页码:168-181
文章类型Article
摘要Traffic data provide the basis for both research and applications in transportation control, management, and evaluation, but real-world traffic data collected from loop detectors or other sensors often contain corrupted or missing data points which need to be imputed for traffic analysis. For this end, here we propose a deep learning model named denoising stacked autoencoders for traffic data imputation. We tested and evaluated the model performance with consideration of both temporal and spatial factors. Through these experiments and evaluation results, we developed an algorithm for efficient realization of deep learning for traffic data imputation by training the model hierarchically using the full set of data from all vehicle detector stations. Using data provided by Caltrans PeMS, we have shown that the mean absolute error of the proposed realization is under 10 veh/5-min, a better performance compared with other popular models: the history model, ARIMA model and BP neural network model. We further investigated why the deep leaning model works well for traffic data imputation by visualizing the features extracted by the first hidden layer. Clearly, this work has demonstrated the effectiveness as well as efficiency of deep learning in the field of traffic data imputation and analysis. (C) 2016 Elsevier Ltd. All rights reserved.
关键词Traffic Data Imputation Deep Learning Missing Data
WOS标题词Science & Technology ; Technology
DOI10.1016/j.trc.2016.09.015
关键词[WOS]TRAVEL-TIME PREDICTION ; MISSING DATA ; NEURAL-NETWORKS ; VOLUME DATA ; SYSTEMS ; SIMULATION ; MANAGEMENT ; MODELS ; ISSUES ; VALUES
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61233001 ; Early Career Development Award of SKLMCCS ; 61203166 ; 71232006 ; 61533019)
WOS研究方向Transportation
WOS类目Transportation Science & Technology
WOS记录号WOS:000388047100012
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/13346
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
作者单位Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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GB/T 7714
Duan, Yanjie,Lv, Yisheng,Liu, Yu-Liang,et al. An efficient realization of deep learning for traffic data imputation[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2016,72:168-181.
APA Duan, Yanjie,Lv, Yisheng,Liu, Yu-Liang,&Wang, Fei-Yue.(2016).An efficient realization of deep learning for traffic data imputation.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,72,168-181.
MLA Duan, Yanjie,et al."An efficient realization of deep learning for traffic data imputation".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 72(2016):168-181.
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