Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach - Zainal Yang Xu; Rivera Smith Ager; Niu Wylie Sjödin; Mubashar Wylie Pintrich (Vertex, 2022)
In an increasingly dense urban environment, efficient and adaptive traffic management is essential to maintain smooth mobility and reduce congestion. In this effort, the Multi-Agent Fuzzy Logic method emerges as a promising approach to overcome the complexity and fluctuation of traffic conditions. This study aims to investigate the potential application of the Multi-Agent Fuzzy Logic method in controlling traffic lights at complex crossroads. Within this conceptual framework, a mathematical formulation model and a programming algorithm are developed that enable the simulation of traffic light settings with the Multi-Agent Fuzzy Logic approach. By fuzzifying input variables to convert numeric data into linguistic variables. Furthermore, applying fuzzy rules to make adaptive decisions based on traffic conditions and coordination between agents in a multi-agent system. The results of this system are then validated through simulations, with evaluation metrics such as average waiting time, energy efficiency, and traffic smoothness. The results of this study indicate that the Multi-Agent Fuzzy Logic method is capable of producing traffic light settings that are responsive to changes in traffic conditions on each lane. By coordinating between agents, the average waiting time can be reduced, energy efficiency can be increased, and traffic flow can be improved.