Modeling of Merging Decision during Execution Period Based on Random Forest

Abstract

This study aims to investigate the key feature variables and build an accurate decision model for merging behavior during the execution period by using a data-driven method called random forest (RF). To comprehensively explore the feature variables during merging execution period, nineteen candidate variables including speeds, relative speeds, gaps, time-to-collisions (TTCs), and locations are extracted from a dataset including 375 noise-filtered vehicle trajectories. After the variable selection process, an RF model with 9 key feature variables is finally built. Results show that the gap between the merging vehicle and its putative following vehicle and the ration of this gap to the total accepted gap are the two most important feature variables. It is because merging vehicle drivers can easily observe the putative leading vehicles and control the relative speeds and positions to the putative leading vehicles and they tend to leave more space for their putative following vehicles. Relative speed between the merging vehicle and its following vehicle in the auxiliary lane is the only variable related to the vehicles in the auxiliary lane, which means merging vehicles mainly focus on the traffic condition in the adjacent main lane. Evaluation of the performance in comparison with the state-of-the-art method reveals that the proposed method can obtain much more accurate results in both training and testing datasets, which means RF is practical for predicting the merging decision behavior during execution period and has better transferability.