Development of a modified fuzzy logic-based system in decongesting traffic at road junctions

Abstract

Traffic congestion at road junctions is a growing concern in urban areas, significantly impacting travel time, fuel consumption, and environmental pollution. As cities expand and vehicle ownership increases, traditional traffic management systems struggle to handle the rising volume of vehicles, leading to frequent bottlenecks at key intersections. Existing traffic decongestion methods, such as fuzzy-based algorithms, suffer from design complexity and tuning inaccuracy. To address these limitations, this work proposes a modified fuzzy logic-based algorithm integrated with the Spider Wasp Optimization (SWO) algorithm for efficient traffic decongestion at road junctions. Traffic parameters including vehicle arrival rate, queue length, and waiting time were generated using a MATLAB R2023a-based stochastic traffic simulation model. These inputs fed into a fuzzy logic controller that determined adaptive green signal durations for each lane. The SWO algorithm, modeled on the predatory and resource allocation behavior of spider wasps, was employed to optimize the fuzzy rule weights and membership function parameters. System performance was evaluated using queue length, average vehicle delay, throughput, signal timing efficiency, green time utilization, and intersection delay index as performance metrics. Comparative simulation results demonstrated that the proposed hybrid SWO-fuzzy system outperformed the standalone fuzzy logic controller by reducing congestion, improving signal utilization efficiency, and enhancing traffic flow stability. The developed model exhibited adaptive capability to varying traffic scenarios without human intervention, thereby improving road safety, reducing fuel consumption, and enhancing commuter experience.