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An efficient realization of deep learning for traffic data imputation

文献类型：期刊论文

作者Duan, Yanjie; Lv, Yisheng; Liu, Yu-Liang; Wang, Fei-Yue
刊名TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
出版日期2016-11-01
卷号72页码:168-181
关键词Traffic data imputation Deep learning Missing data
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Transportation Science & Technology
研究领域[WOS]Transportation
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
WOS记录号WOS:000388047100012
源URL[http://ir.ia.ac.cn/handle/173211/13346]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
作者单位Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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