IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data. As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative mo...

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Bibliographic Details
Main Authors: Amir Kazemi, Hadi Meidani
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Generative adversarial networks
GAN
missing data
imputation
invertible neural networks (INN)
Online Access:https://ieeexplore.ieee.org/document/9510088/

Internet

https://ieeexplore.ieee.org/document/9510088/

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