Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these methods treats every candidate signal phase as equally worth trying, discarding the rich domain knowledge that traffic theory already provides. This paper proposes fuzzy-guided exploration, in which a multi-criteria fuzzy inference system uses local traffic conditions, with phase pressure as its primary input, to assign each candidate phase a priority. These priorities define a sampling distribution used in place of the uniform draw. We evaluate the method across four MARL algorithms covering independent learning (IQL) and the centralized training with decentralized execution paradigm (VDN, QMIX, and QPLEX) on both a synthetic grid and a real-world network. Fuzzy-guided exploration consistently improves upon the baseline in all combinations, with tangible gains on the synthetic grid and substantially larger improvements on the real-world network. These findings demonstrate that exploration is an effective intervention point for domain-knowledge integration in cooperative MARL, and that pressure-based scoring provides a well-suited signal to serve that role in traffic signal control.

This paper is described different strategies for traffic control and simulation, namely - Vehicle Actuated (VA) compared to Fixed Time (FT) traffic control. We are comparing and evaluating the impact of FT and adaptive traffic signal control strategies for one of the biggest and busiest intersections in Tashkent, the capital of Uzbekistan. The parameters used for comparing these strategies are the average vehicle travel times and average vehicle queue lengths. TRL TRANSYT 15 software was used to determine the fixed signal timing plan based on intersection geometry, inter-green matrix, and traffic flow. PTV VISSIM software is used for VA algorithm development and simulation to get the maximum and average vehicle travel times as well as queue lengths. It is shown that the parameter optimization can reduce the queue lengths down to 65% and the travel time down to 51%.