Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis

TL;DR

This paper demonstrates that a time series foundation model, Chronos, can effectively analyze and predict car-following behavior, outperforming traditional models and matching deep learning methods with minimal fine-tuning.

Contribution

The study applies a state-of-the-art time series foundation model to car-following behavior analysis, showing its superior performance and adaptability without extensive re-training.

Findings

Chronos outperforms traditional models like IDM and ETS without fine-tuning.

Fine-tuned Chronos reduces RMSE by 33.75% over IDM.

Foundation models offer scalable, accurate predictions for traffic behavior analysis.

Abstract

Modeling car-following behavior is essential for traffic simulation, analyzing driving patterns, and understanding complex traffic flows with varying levels of autonomous vehicles. Traditional models like the Safe Distance Model and Intelligent Driver Model (IDM) require precise parameter calibration and often lack generality due to simplified assumptions about driver behavior. While machine learning and deep learning methods capture complex patterns, they require large labeled datasets. Foundation models provide a more efficient alternative. Pre-trained on vast, diverse time series datasets, they can be applied directly to various tasks without the need for extensive re-training. These models generalize well across domains, and with minimal fine-tuning, they can be adapted to specific tasks like car-following behavior prediction. In this paper, we apply Chronos, a state-of-the-art public time series foundation model, to analyze car-following behavior using the Open ACC dataset. Without fine-tuning, Chronos outperforms traditional models like IDM and Exponential smoothing with trend and seasonality (ETS), and achieves similar results to deep learning models such as DeepAR and TFT, with an RMSE of 0.60. After fine-tuning, Chronos reduces the error to an RMSE of 0.53, representing a 33.75% improvement over IDM and a 12-37% reduction compared to machine learning models like ETS and deep learning models including DeepAR, WaveNet, and TFT. This demonstrates the potential of foundation models to significantly advance transportation research, offering a scalable, adaptable, and highly accurate approach to predicting and simulating car-following behaviors.