An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation
S. Narmadha1, V. Vijayakumar2

1S.Narmadha, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India.
2Dr V.Vijayakumar, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore, India.
Manuscript received on 25 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 3951-3954 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20220981119/2019©BEIESP | DOI: 10.35940/ijitee.K2022.0981119
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Abstract: Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.
Keywords: Spatio-Temporal, Deep Learning, Elu Activation, Missing value, AutoEncoder
Scope of the Article: Network Traffic Characterization and Measurements