Evaluation of the Gradient Boosting of Regression Trees Method on Estimating the Car Following Behavior

TL;DR

This paper evaluates the Gradient Boosting Regression Tree (GBRT) method for modeling car-following behavior, demonstrating its superior performance over traditional models in predicting vehicle trajectories using real-world data.

Contribution

It introduces the application of GBRT to car-following modeling and compares its effectiveness with the GHR model using actual trajectory data.

Findings

GBRT outperforms GHR in predicting vehicle motion.

Regularization parameters are effectively tuned via cross-validation.

GBRT captures complex vehicle interactions better than traditional models.

Abstract

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.