| Abstract |
In the ever-evolving landscape of smart city transportation, effective traffic management remains a critical challenge. To address this, we propose a novel Smart Traffic Management System (STMS) Architecture algorithm that combines cutting-edge technologies, including Blockchain, IoT, edge computing, and reinforcement learning. STMS aims to optimize traffic flow, minimize congestion, and enhance transportation efficiency while ensuring data integrity, security, and decentralized decision-making. STMS integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) reinforcement learning algorithm with Blockchain technology to enable secure and transparent data sharing among traffic-related entities. Smart contracts are deployed on the Blockchain to automate the execution of predefined traffic rules, ensuring compliance and accountability. Integrating IoT sensors on vehicles, roadways, and traffic signals provides real-time traffic data, while edge nodes perform local traffic analysis and contribute to optimization. The algorithm's decentralized decision-making empowers edge devices, traffic signals, and vehicles to interact autonomously, making informed decisions based on local data and predefined rules stored on the Blockchain. TD3 optimizes traffic signal timings, route suggestions, and traffic flow control, ensuring smooth transportation operations. STMSs holistic approach addresses traffic management challenges in smart cities by combining advanced technologies. By leveraging Blockchain's immutability, IoT's real-time insights, edge computing's local intelligence, and TD3's reinforcement learning capabilities, STMS presents a robust solution for achieving efficient and secure transportation systems. This research underscores the potential for innovative algorithms to revolutionize urban mobility, ushering in a new era of smart and sustainable transportation networks. |
