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

arXiv: https://arxiv.org/abs/2501.07034 · HTML: https://mlsanity.com/paper/2501.07034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [Macroeconomic Forecasting with Large Language Models](https://mlsanity.com/paper/2407.00890/) (2024)
- [Platoon trajectories generation: A unidirectional interconnected LSTM-based car following model](https://mlsanity.com/paper/1910.11843/) (2019)
- [Calibrating microscopic car following models for adaptive cruise control vehicles: a multi-objective approach](https://mlsanity.com/paper/2010.03109/) (2020)
- [A Unified Longitudinal Trajectory Dataset for Automated Vehicle](https://mlsanity.com/paper/2406.00009/) (2024)

## Abstract (arXiv)

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.