Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparative Analysis of Machine Learning Models for Predicting Travel
  Time   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.

27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn