Route Agnostic Estimated Time of Arrival in Vehicle Trip Using Machine Learning


Authors : YMPHAIDIEN SUTONG; RAMNEET S. CHADHA

Volume/Issue : Volume 7 - 2022, Issue 8 - August

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3c9rna6

DOI : https://doi.org/10.5281/zenodo.7011705

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.

Keywords : Estimated Time of Arrival, Regression, Gradient Boosting, Random Forest, Artificial Intelligence, Transportation, Light Gradient Boosted Machine.

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Paper Submission Last Date
29 - February - 2024

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