Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis

TL;DR

This paper demonstrates that general-purpose time-series foundation models like Chronos-2 can serve as strong zero-shot baselines for transportation forecasting, often outperforming specialized models across diverse real-world datasets.

Contribution

It provides a comprehensive benchmark showing that Chronos-2 achieves state-of-the-art or competitive zero-shot performance in transportation forecasting without dataset-specific training.

Findings

Chronos-2 outperforms classical and deep learning models at longer horizons.

It provides reliable uncertainty quantification without fine-tuning.

Chronos-2 achieves competitive accuracy across ten diverse transportation datasets.

Abstract

Accurate forecasting of transportation dynamics is essential for urban mobility and infrastructure planning. Although recent work has achieved strong performance with deep learning models, these methods typically require dataset-specific training, architecture design and hyper-parameter tuning. This paper evaluates whether general-purpose time-series foundation models can serve as forecasters for transportation tasks by benchmarking the zero-shot performance of the state-of-the-art model, Chronos-2, across ten real-world datasets covering highway traffic volume and flow, urban traffic speed, bike-sharing demand, and electric vehicle charging station data. Under a consistent evaluation protocol, we find that, even without any task-specific fine-tuning, Chronos-2 delivers state-of-the-art or competitive accuracy across most datasets, frequently outperforming classical statistical baselines and specialized deep learning architectures, particularly at longer horizons. Beyond point forecasting, we evaluate its native probabilistic outputs using prediction-interval coverage and sharpness, demonstrating that Chronos-2 also provides useful uncertainty quantification without dataset-specific training. In general, this study supports the adoption of time-series foundation models as a key baseline for transportation forecasting research.