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A Comparison of ARIMAX, VAR and LSTM on Multivariate Short-Term Traffic Volume Forecasting
2021
Zenodo
Traffic volume forecasting is a key objective in Intelligent Transportation Systems (ITS) since its importance for both the general public and authorities in decision making, optimizing navigation strategies and avoid traffic congestions. Various research projects have been conducted for identifying the best approach to solve that issue. This paper proposes a comparison of statistical learning models, Vector Auto Regression, ARIMAX and a deep learning model, LSTM neural network, in the context
doi:10.5281/zenodo.4514955
fatcat:lbkucqzgzvau3cdl2xyag5none