  In this paper, five different deep learning models are being compared for
predicting travel time. These models are autoregressive integrated moving
average (ARIMA) model, recurrent neural network (RNN) model, autoregressive
(AR) model, Long-short term memory (LSTM) model, and gated recurrent units
(GRU) model. The aim of this study is to investigate the performance of each
developed model for forecasting travel time. The dataset used in this paper
consists of travel time and travel speed information from the state of
Missouri. The learning rate used for building each model was varied from
0.0001-0.01. The best learning rate was found to be 0.001. The study concluded
that the ARIMA model was the best model architecture for travel time prediction
and forecasting.
