TLMOF: An Operating Framework Inspired Hybrid Kernel for Signal Control
Urban traffic congestion imposes significant economic and environmental costs worldwide. Conventional adaptive signal controllers-including actuated systems and deep reinforcement-learning (DRL) approaches-either collapse under saturation or lack formal verifiability, making them unsuitable for safety-critical infrastructure. This paper presents the Traffic Light Management Operating Framework (TLMOF), a signal control architecture that maps classical operating-system (OS) scheduling theory directly onto intersection management. Vehicles are modeled as processes, approach lanes as ready queues, and the intersection controller as a CPU scheduler. The TLMOF Hybrid Kernel selects signal phases by maximising a modified pressure function combining Max-Pressure (MP) throughput control with Weighted Fair Queuing (WFQ) anti-starvation. Gridlock is formally characterised as a Coffman deadlock, detected via Wait-for-Graph (WFG) depth-first search in O(V+E) time, and resolved through a formally specified Flush Phase. With fairness weight β > 0, the kernel provably guarantees bounded delay for all movements (AntiStarvation Theorem). SUMO 1.26 validation across three demand scenarios demonstrates a 377× average-delay reduction over fixed-time under saturation and a 25% Jain's Fairness Index improvement over pure Max-Pressure (0.909 vs. 0.727) under asymmetric demand while reducing average delay by 56% (3.62 s vs. 8.15 s). A sensitivity analysis of (α, β) confirms that the recommended default (0.7, 0.3) achieves a robust balance between throughput and fairness.