Foundation Model-Based Predictive Analytics for Intelligent Transportation Systems

Abstract

Intelligent Transportation Systems (ITS) are undergoing a paradigm shift driven by the unprecedented volume of multi-modal data and the necessity for highly accurate, real-time predictive analytics. Traditional machine learning and deep learning architectures, while effective for localized tasks, often struggle with spatial-temporal distribution shifts, cross-city generalization, and the integration of heterogeneous data sources such as traffic cameras, GPS trajectories, weather reports, and textual incident logs. This paper investigates the application of Foundation Models (FMs) large-scale architectures pre-trained on massive datasets and fine-tuned for downstream tasks as the core engine for next-generation ITS predictive analytics. The article presents a generic framework which exploits the zero-shot, few-shot and multi-modal capabilities of foundation models to forecast traffic flow, predict traffic incidents and improve public transit schedules. This study uses large-scale comparisons and empirical simulations to evaluate the structural benefits, integration techniques, and performance trade-offs of FMs against common deep learning frameworks. The results show that foundation model-based methods achieve better generalization on unseen urban networks and high robustness towards the noisy or missing sensor data. Lastly, we identify some of the principal two-sided challenges including computational overhead, latency constraints in edge deployment and privacy protection, also discuss a few strategies for addressing these issues and direction towards new research and deployable practices within smart city infrastructures.