Efficient Graph-Based Algorithm for Real-Time Traffic Flow Optimization in Smart Cities

With the rapid increase in vehicle density, urban traffic congestion has become a significant challenge, leading to inefficiencies in transportation systems and increased fuel consumption. Existing traffic management methods, such as fixed signal timing and heuristic-based optimizations, struggle to adapt to real-time traffic fluctuations. While recent studies have explored graph-based models and reinforcement learning for traffic optimization, they often fail to capture complex spatiotemporal dependencies dynamically. To address this gap, we propose a novel graph-based algorithm that integrates spatiotemporal graph convolutional networks (ST-GCN) with reinforcement learning techniques. The algorithm dynamically adjusts traffic signals, reroutes vehicles, and provides real-time guidance based on sensor and environmental data. Experimental evaluations using a simulated urban traffic environment demonstrate that our approach significantly reduces vehicle waiting times and improves overall traffic flow compared to traditional methods. These findings highlight the potential of adaptive, intelligent traffic management systems for smart cities.