Open AccessAn Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation
Author(s) -
S. Narmadha,
Dr V. Vijayakumar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k2022.0981119
Subject(s) - autoencoder , missing data , imputation (statistics) , computer science , data mining , artificial intelligence , deep learning , activation function , pattern recognition (psychology) , machine learning , artificial neural network
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.