<p>Urban traffic congestion remains a critical challenge in smart cities, particularly in dense networks of closely spaced intersections. Traditional fixed-timing signals cannot adapt to real-time traffic fluctuations, often resulting in inefficiencies and congestion spillback. Meanwhile, purely local adaptive systems improve intersection responsiveness but still lack coordinated, network-wide optimization. To address these gaps, this paper proposes Hybrid Distributed Adaptive Signal Coordination (Hybrid-DASC), a two-tier framework that combines decentralized real-time signal control with centralized coordination via Software-Defined Networking (SDN) to dynamically enable green waves on demand. At each intersection, the system blends live traffic measurements with SDN-predicted arrivals using tunable parameters (α, β, γ) to optimize green durations, balancing immediate local demand with anticipated upstream flows under safety constraints. The Hybrid-DASC approach is evaluated through SUMO simulations on a realistic 8 × 8 urban traffic grid. Results show that Hybrid-DASC outperforms fixed-time and conventional local-adaptive control across all key performance metrics. It significantly lowers average waiting time and queue length while substantially increasing throughput, and achieves markedly higher green-wave success rates with significantly fewer multi-cycle clearance failures. Throughout these coordinated optimizations, no safety violations or phase conflicts occur, reflecting robust operation. Overall, Hybrid-DASC delivers scalable, safe, and coordinated traffic control, offering an effective solution for intelligent traffic signal control in modern smart city infrastructures.</p>