An Intelligent Hybrid Learning Framework Integrating ANFIS, Genetic Algorithms, and Reinforcement Learning for Traffic Signal Control - Dodo, Enoch Jacob; Onwodi, Gregory; Okure, Obot (Journal of Science Research and Reviews, 2025)
Rapid urbanization and increasing vehicular demand have intensified traffic congestion, exposing the limitations of conventional static traffic signal control systems. This study proposes a novel hybrid intelligent traffic control framework that integrates an Adaptive Neuro-Fuzzy Inference System, Genetic Algorithm, and Reinforcement Learning (ANFIS–GA–RL) to achieve real-time adaptive signal optimization. The proposed approach uniquely combines interpretable fuzzy reasoning for managing uncertainty, genetic algorithms for global parameter optimization, and reinforcement learning for closed-loop, real-time decision-making, distinguishing it from existing standalone and partially hybrid methods. Performance is evaluated in a MATLAB-based urban traffic simulation using eight performance indicators, travel time, average vehicle speed, throughput, traffic density, queue length, delay time, intersection delay, and computational time. Comparative results against conventional Fuzzy Logic, standalone Genetic Algorithm, Artificial Neural Network, ANFIS, and ANFIS–GA controllers demonstrate consistent and measurable performance gains. Relative to the baseline fuzzy logic controller, the proposed ANFIS–GA–RL model achieves an overall improvement of 64.9%, characterized by substantial reductions in travel time delay, intersection delay, and computational overhead, alongside enhanced throughput and traffic flow stability. These findings confirm the robustness, scalability, and real-time applicability of the proposed framework for intelligent urban traffic signal control, with future work focusing on IoT-enabled deployment and field validation.
Keywords: Intelligent Traffic Signal Control; Reinforcement Learning; Traffic Congestion Mitigation; Hybrid Optimization; ANFIS–GA–RL framework