Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in...

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Bibliographic Details
Main Authors: Tongge Huang, Pranamesh Chakraborty, Anuj Sharma
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2023-03-01
Series:International Journal of Transportation Science and Technology
Subjects:
Traffic data imputation
Generative adversarial networks
Realistic data generation
Time-dependent encoding
Deep convolutional neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S2046043021000824

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http://www.sciencedirect.com/science/article/pii/S2046043021000824

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