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.

翻译：准确预测交通动态对于城市出行与基础设施规划至关重要。尽管近期研究通过深度学习模型取得了优异性能，但这些方法通常需要针对特定数据集进行训练、架构设计与超参数调优。本文通过评估当前最先进的模型 Chronos-2 在十个真实世界数据集上的零样本性能，检验通用时间序列基础模型能否作为交通任务的预测器。这些数据集涵盖高速公路交通流量与车流、城市交通速度、共享单车需求及电动汽车充电站数据。在统一的评估协议下，我们发现即使未进行任何任务特定微调，Chronos-2 在多数数据集上仍能实现最优或具有竞争力的精度，尤其在较长预测范围内，其表现经常超越经典统计基线与专用深度学习架构。除点预测外，我们通过预测区间覆盖度与锐度评估其原生概率输出，证明 Chronos-2 无需数据集特定训练即可提供有效的概率量化。总体而言，本研究支持将时间序列基础模型作为交通预测研究的关键基线。