Toward Safe and Scalable Intelligent Transportation Systems: A Survey of AI Methodologies and Deployment Challenges

Abstract

Artificial Intelligence (AI) is fundamentally reshaping Intelligent Transportation Systems (ITS) by enabling real-time perception, prediction, and decision-making for connected and autonomous mobility networks. The convergence of deep learning, reinforcement learning, and computer vision with advanced sensing and communication technologies empowers vehicles and infrastructure to collaboratively manage traffic, improve safety, and reduce environmental impact. AI-driven methods have demonstrated superiority over traditional rule-based approaches in traffic forecasting, route optimization, and autonomous vehicle control, while edge computing and 5G/6G connectivity are making large-scale deployment increasingly feasible. However, challenges remain in data heterogeneity, model interpretability, safety validation, cybersecurity, and regulatory compliance. This survey provides a comprehensive review of AI methodologies applied to ITS, covering traffic prediction, perception for autonomous driving, multi-agent control, and smart infrastructure optimization. We analyze the current state of deployment, highlight open challenges such as privacy-preserving learning and robust decision-making under uncertainty, and propose future research directions including AI-enabled digital twins, federated learning for vehicular networks, and scalable edge intelligence.