  Car-following models, as the essential part of traffic microscopic
simulations, have been utilized to analyze and estimate longitudinal drivers'
behavior since sixty years ago. The conventional car following models use
mathematical formulas to replicate human behavior in the car-following
phenomenon. Incapability of these approaches to capturing the complex
interactions between vehicles calls for deploying advanced learning frameworks
to consider the more detailed behavior of drivers. In this study, we apply the
Gradient Boosting of Regression Tree (GBRT) algorithm to the vehicle trajectory
data sets, which have been collected through the Next Generation Simulation
program, so as to develop a new car-following model. First, the regularization
parameters of the proposed method are tuned using the cross-validation
technique and the sensitivity analysis. Afterward, the prediction performance
of the GBRT is compared to the world-famous GHR model, when both models have
been trained on the same data sets. The estimation results of the models on the
unseen records indicate the superiority of the GBRT algorithm in capturing the
motion characteristics of two successive vehicles.
