A Data-Driven Car-Following Model Based on the Random Forest

Tingli Wang,Hanqing Wang,Huili Shi,Junyan Han,Xiaoyuan Wang,Fusheng Zhong

doi:10.4236/wjet.2021.93033

Abstract

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.

Highlights

The traffic flow theory is the theoretical basis for analyzing the operation mechanism of traffic flow under different traffic conditions to effectively organize and manage the transportation system
Mean Error (ME), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) reflect the degree of the error, while Mean Absolute Relative Error (MARE) reflects the proportion of the error in the samples
Among the data-driven models, compared with the Artificial Neural Network (ANN) model commonly used in previous research, the performance improvement of the model proposed in this work can reach up to 77.282%

Summary

Introduction

The traffic flow theory is the theoretical basis for analyzing the operation mechanism of traffic flow under different traffic conditions to effectively organize and manage the transportation system. As the most basic driving behavior, the modeling study on car-following behavior is one of the core research contents of traffic flow theory, and it has received extensive attention from researchers from multiple research fields [1] [2]. Compared with another common driving behavior model (i.e. the lane-changing model [3] [4]), the car-following model describes the longitudinal behavior of vehicles in the current lane, which is very common in the restricted overtaking section (such as ramp) and the continuous-flow facilities (such as the highway). As a typical integrated machine learning method, the RF [24] has shown very high performance in many fields [25] [26] [27] [28]

Results

Discussion

Conclusion