A Secure and Explainable Multi-Agent Reinforcement Learning Framework with Improved Hawk Optimization for Intelligent Traffic Management in VANET-Enabled Smart Cities

Abstract

In the context of growing urbanization and vehicular density, Vehicular Ad-Hoc Networks (VANETs) are essential for enabling intelligent traffic management in smart cities. Real-time adaptability, decision transparency, and secure communication remain major challenges. This research proposes a novel framework that integrates Secure and Explainable Multi-Agent Reinforcement Learning (XMARL) with an Improved Hawk Optimization (IHO) algorithm to address traffic dynamics in VANET environments. Key issues targeted include high congestion, inefficient routing, lack of interpretability in AI decisions, and vulnerability to cyber-attacks. In the proposed system, each vehicle and Road-Side Unit (RSU) functions as an autonomous agent trained using XMARL for decentralized decision-making. IHO enhances learning by adaptively tuning policy parameters, optimizing signal timings, and refining route selection based on real-time conditions. To ensure interpretability, Explainable AI techniques such as SHAP values and attention mechanisms are integrated, providing human-understandable insights into agent actions. A lightweight blockchain layer secures communications, ensuring data integrity and trust between nodes. The framework aims to deliver a scalable, interpretable, and secure traffic management solution capable of reducing congestion, improving travel efficiency, and resisting malicious threats. Simulation results using SUMO and Veins demonstrate that the proposed model outperforms existing XMARL methods in traffic throughput, decision transparency, learning convergence, and packet delivery ratio under diverse urban scenarios. This integrated approach presents advancement toward building trustworthy and efficient smart transportation systems.