An efficient realization of deep learning for traffic data imputation
Duan, Yanjie; Lv, Yisheng; Liu, Yu-Liang; Wang, Fei-Yue
Source PublicationTRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
2016-11-01
Volume72Pages:168-181
SubtypeArticle
AbstractTraffic 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.
KeywordTraffic Data Imputation Deep Learning Missing Data
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.trc.2016.09.015
WOS KeywordTRAVEL-TIME PREDICTION ; MISSING DATA ; NEURAL-NETWORKS ; VOLUME DATA ; SYSTEMS ; SIMULATION ; MANAGEMENT ; MODELS ; ISSUES ; VALUES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61233001 ; Early Career Development Award of SKLMCCS ; 61203166 ; 71232006 ; 61533019)
WOS Research AreaTransportation
WOS SubjectTransportation Science & Technology
WOS IDWOS:000388047100012
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13346
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
AffiliationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
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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