|An efficient realization of deep learning for traffic data imputation|
|Duan, Yanjie; Lv, Yisheng; Liu, Yu-Liang; Wang, Fei-Yue
|Source Publication||TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
|Abstract||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.|
|Keyword||Traffic Data Imputation
|WOS Headings||Science & Technology
|WOS Keyword||TRAVEL-TIME PREDICTION
; MISSING DATA
; VOLUME DATA
|Funding Organization||National Natural Science Foundation of China(61233001
; Early Career Development Award of SKLMCCS
|WOS Research Area||Transportation
|WOS Subject||Transportation Science & Technology
|Affiliation||Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China|
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.
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.
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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