Novel Urban Mobility Management Model with Dynamic Learning and Swarm Optimization for Neural Traffic Orchestration

A solution for neural traffic congestion is necessary to ensure efficient and reliable communication within neural networks, minimizing delays and disruptions that can impede the performance of critical tasks and applications. Additionally, optimizing traffic flow is essential for maximizing resource utilization and overall network efficiency, especially in large-scale systems handling complex data processing tasks. The proposed work outlines a solution for neural traffic congestion using Particle Swarm Optimization (PSO) and a neural network-based traffic orchestration system. PSO operates by simulating a swarm of particles in a multidimensional search space, optimizing parameters influencing traffic flow. It strikes a balance between exploration and exploitation, gradually converging towards optimal configurations. The neural network orchestrates traffic by predicting congestion, rerouting packets, and maintaining efficient communication. The system's objective function minimizes congestion and delays across network nodes. Through training and validation, the neural network learns to optimize traffic flow, while PSO dynamically adjusts parameters to adapt to changing conditions. Multiple paths in the network enhance redundancy and fault tolerance, allowing for load balancing and resilience to failures. Experimental validation involves analyzing path performance under varying traffic loads and failure scenarios. This integrated approach aims to ensure efficient and reliable neural communication while mitigating congestion-induced disruptions. Accuracy surged from 0.75 to 0.85, showcasing enhanced precision in traffic prediction. Convergence rate accelerated from 0.6 to 0.8, indicating quicker attainment of optimal solutions. Moreover, training time plummeted from 120 units to 90 units, reflecting streamlined and efficient training processes.