Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast

Volume: 111, Pages: 352 - 372
Published: Feb 1, 2020
Abstract
We propose a novel approach for network-wide traffic state prediction where the statistical time series model ARIMA is used to postprocess the residuals out of the fundamental machine learning algorithm MLP. This approach is named as NN-ARIMA. Neural Network MLP is employed to capture network-scale co-movement pattern of all traffic flows, and ARIMA is used to further extract location-specific traffic features in the residual time series out of...
Paper Details
Title
Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast
Published Date
Feb 1, 2020
Volume
111
Pages
352 - 372
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