Multi-Variable Traffic Control: Simulating the Impact of Weather and Incidents via Type-1 Fuzzy Logic

Abstract

Basic The effective management of urban traffic remains an important obstacle, due to the dynamic and probabilistic nature of typical urban settings. Conventional traffic control systems, which are primarily based on fixed-timing or simple sensor-actuated adaptive mechanisms, frequently disregard external factors such as adverse weather conditions and unexpected road incidents. In this research, a multi-variable traffic control framework is proposed. The system is designed to maximize the intersection throughput by incorporating the multiple variables into the fuzzy logic simulation system. The developed system uses fuzzy logic controller that can handle the linguistic variables and non-linear inputs. Inputs are traffic density, precipitation intensity, visibility conditions, and incident severity. The controller employs a large set of rules to adaptively adjust signal timings to compensate for the reduced roadway capacity and longer stopping distances that are common in rain or fog. Simulation analyses were carried out to evaluate the performance of the fuzzy logic model compared to the existing control methods. The results demonstrate that the multi-variable fuzzy strategy performs significantly better than conventional methods, particularly under peak demand and extreme weather conditions.