Modeling Electric Vehicle Car-Following Behavior: Classical vs Machine Learning Approach

TL;DR

This paper compares classical and machine learning models for electric vehicle car-following behavior, demonstrating that a Random Forest approach outperforms traditional physics-based models in prediction accuracy using real-world data.

Contribution

The study introduces a machine learning model for EV car-following behavior and compares its performance with classical models using real-world data, highlighting the advantages of machine learning.

Findings

Random Forest achieved lower RMSE than classical models.

Machine learning models outperform physics-based models in accuracy.

Results support using ML for simulating EV behavior in traffic systems.

Abstract

The increasing adoption of electric vehicles (EVs) necessitates an understanding of their driving behavior to enhance traffic safety and develop smart driving systems. This study compares classical and machine learning models for EV car following behavior. Classical models include the Intelligent Driver Model (IDM), Optimum Velocity Model (OVM), Optimal Velocity Relative Velocity (OVRV), and a simplified CACC model, while the machine learning approach employs a Random Forest Regressor. Using a real world dataset of an EV following an internal combustion engine (ICE) vehicle under varied driving conditions, we calibrated classical model parameters by minimizing the RMSE between predictions and real data. The Random Forest model predicts acceleration using spacing, speed, and gap type as inputs. Results demonstrate the Random Forest's superior accuracy, achieving RMSEs of 0.0046 (medium gap), 0.0016 (long gap), and 0.0025 (extra long gap). Among physics based models, CACC performed best, with an RMSE of 2.67 for long gaps. These findings highlight the machine learning model's performance across all scenarios. Such models are valuable for simulating EV behavior and analyzing mixed autonomy traffic dynamics in EV integrated environments.