Estimating Time of Driver Arrival with Gradient Boosting Algorithms and Deep Neural Networks

Customer experience and resource management determine the degree to which transportation service providers can compete in today’s heavily saturated markets. The paper investigates and suggests a new methodology to optimize calculations for Estimated Time of Arrival (from now on ETA, meaning the time it will take for the driver to reach the designated location) based on the data provided by GG collected from rides made in 2018. GG is a transportation service providing company, and it currently uses The Open Source Routing Machine (OSRM) which exhibits significant errors in the prediction phase. This paper shows that implementing algorithms such as XGBoost, CatBoost, and Neural Networks for the said task will improve the accuracy of estimation. Paper discusses the benefits and drawbacks of each model and then considers the performance of the stacking algorithm that combines several models into one. Thus, using those techniques, final results showed that Mean Squared Error (MSE) was decreased by 54% compared to the current GG model.