Learning Spatio-Temporal Foundation Models for Traffic Prediction from Pure Synthetic Data

Spatio-temporal foundation models (STFMs) offer a promising path toward universal traffic forecasting by learning transferable priors across heterogeneous road networks and cities. However, existing approaches suffer from distributional bias in real-world pre-training data, structural bottlenecks of autoregressive or diffusion-based paradigms, and objectives that overemphasize point-wise reconstruction in noisy observation space. We propose NeoST, the first STFM for traffic forecasting pre-trained solely on procedurally generated synthetic systems. NeoST introduces a scalable synthetic pre-training corpus to mitigate real-world bias, a latent-space reasoning architecture that generates and iteratively refines multiple future trajectories without sequential error accumulation, and latent-space objectives that emphasize structural dynamics and enable inference-time correction under distribution shifts. Extensive experiments across diverse real-world traffic benchmarks show that NeoST consistently outperforms existing STFMs in diverse real-world traffic systems, achieves superior long-horizon stability and inference efficiency.