OptiFlow: Optimizing Traffic Flow in ITS With Improved Cluster Routing

Abstract

Intelligent Transport Systems (ITS) rely heavily on Vehicular Ad hoc Networks (VANET) to facilitate effective communication, especially Vehicle-to-Everything (V2X) communication. However, current research has identified challenges in node management, security, and routing within VANET, calling for bespoke solutions to address these issues. This study introduces an innovative cluster-based routing strategy using Enhanced Slap Swarm Optimization (ESSO) and Evaluation with Mixed Data Multi-criteria Decision-Making (EVAmix MCDM) Method tailored to optimize routing in V2X communication. Unlike existing meta-heuristic methods, which often face slow convergence, premature convergence, and local optima stability, the proposed approach demonstrates striking results. Notably, it enhances throughput by 6278 kbps, elevates the Packet Delivery Ratio (PDR) by 95.77$\%$, and reduces end-to-end delay by 1856ms in the 300th iteration, outperforming existing cluster routing methodologies. Our findings suggest a substantial leap toward surmounting the existing challenges in V2X communication. This innovative solution advances the field and sets a course for real-time applications. This approach allows vehicles to continually monitor, adjust their position, and control their speed on highways, enhancing safety and traffic control.

Keywords

• Clustering
• intelligent transport systems (ITSs)
• meta-heuristic approach
• vehicular ad-hoc networks (VANETs)
• vehicle routing