Smart Convergence System for Traffic and Urban Parking Management

This paper presents a Smart Convergence System for Traffic and Urban Parking Management, integrating AI, IoT, and cloud computing to address urban mobility challenges. As cities grapple with congestion, inefficient parking, and environmental impacts, the system offers a unified solution by: AI-driven traffic control that dynamically adjusts signals using real-time data; Smart parking management with IoT sensors guiding drivers to available spots via a mobile app; Sustainable design, reducing emissions by optimizing traffic flow and parking search time. Case studies in metropolitan, airport, and mall settings demonstrate ۲۰-۳۰% improvements in congestion and parking utilization. The system's scalable architecture supports future integration with autonomous vehicles and smart grids. Challenges like infrastructure costs and data privacy are discussed, alongside solutions such as encryption and modular deployment. By merging traffic and parking management into a single, adaptive platform, this system provides a model for sustainable, user-centric urban mobility.

کلیدواژه ها:

Smart Convergence System ، Urban parking ، Artificial intelligence ، Smart sensors ، IoT