Research in mobility management and mobility models for Vehicular Ad Hoc Networks (VANETs) focuses on improving communication reliability, routing efficiency, and network stability in highly dynamic vehicular environments. Recent studies introduce realistic and context-aware mobility models that capture road topology, traffic density, vehicle behavior, and environmental factors to simulate real-world driving patterns accurately. Machine learning and deep reinforcement learning approaches are increasingly applied for predictive mobility management, enabling proactive handoff, route optimization, and connectivity maintenance. Hybrid and group-based mobility models are also being explored to represent coordinated vehicle movements in urban and highway scenarios. Additionally, edge-assisted and AI-driven mobility management frameworks are developed to enhance scalability, reduce latency, and support intelligent transportation systems. These advancements collectively ensure robust communication, efficient resource utilization, and improved performance in modern VANET environments.