The transportation industry is an important
industry sector in the economy that deals with the
movement of people, goods and products. As vehicles
become safer and more efficient, most individuals and
companies adopt vehicles for midrange traveling, goods
and product transportation and hence, opportunities to
advance transportation abound. To make the most of
them, we need to explore and develop different technology
options. In this study, we explore the potential of Artificial
Intelligence in predicting the Estimated Time of Arrival.
Our method is by modelling historical-data based models.
We find that several Nonlinear Machine Learning
Regression Algorithms like Gradient Boosting Regressor,
Random Forest Regressor, Light Gradient Boosted
Machine, etc are suitable for this problem and are
producing promising results in terms of RMSE and R2.
Out of which the LightGBM model performs best.
Estimated Time of Arrival, Regression, Gradient Boosting, Random Forest, Artificial Intelligence, Transportation, Light Gradient Boosted Machine.