Enhancing Intelligent Transportation Systems in Smart Cities Using VANETs With Deep Reinforcement Transfer Learning and Explainable AI

Abstract

Urban automobile congestion is a persistent issue that reduces the quality of life, increases pollution, and causes financial inefficiencies. Existing traffic management strategies struggle to adapt to rapidly changing urban traffic conditions as they rely on static, rule-based systems. Intelligent Transportation Systems (ITS) operate in highly dynamic environments with intricate temporal and spatial patterns influenced by factors such as weather, social events, and holidays. Accurately modeling these relationships, developing universal representations, and applying them to transportation challenges remain key obstacles. To optimize traffic flow, enhance road safety, and improve decision-making transparency, this study introduces an advanced framework integrating Deep Reinforcement Transfer Learning (DRTL), Vehicular Ad Hoc Networks (VANETs), and Explainable AI (XAI). The goal is to develop an interpretable and adaptable ITS model capable of learning and applying knowledge across diverse traffic scenarios. The DRTL model facilitates rapid adaptation by leveraging pre-trained RL techniques to accelerate learning in complex urban environments. XAI enhances model interpretability, ensuring transparency and reliability in ITS operations. The proposed approach is validated through simulations and real-world traffic data, demonstrating significant improvements in incident detection, route optimization, and congestion forecasting. Compared to conventional machine learning models, the results show a 35% reduction in median congestion, a 40% improvement in real-time route planning, and a 25% enhancement in accident response time. This research contributes to the development of intelligent, adaptive, and safer transportation networks for future smart cities by improving vehicle interactions, decision-making accuracy, and system comprehension.