Efficient traffic management in large metropolitan cities has become an increasingly challenging task due to the exponential rise in transportation demands. The optimization of traffic flow in vehicular ad‐hoc networks (VANETs) to minimize congestion and delays remains an important research area. Numerous studies have proposed different methods and tools for the development of efficient Traffic Management Systems (TMS). However, contemporary methods often show limitations in scalability, adaptability, and responsiveness, particularly in dynamic vehicular environments where rapid changes in traffic patterns are common. This paper proposes a novel and effective TMS by employing a two‐tier architecture that combines fog computing with a centralized system to optimize traffic flow and mitigate congestion. The proposed system integrates the Edmonds–Karp algorithm for maximum flow optimization with clustering techniques implemented at the Roadside Unit (RSU) level. These clustering techniques dynamically classify and manage vehicular requests, enabling real‐time traffic decisions. Extensive evaluation and comparison with existing solutions reveal remarkable improvements in traffic optimization, showing the effectiveness of the proposed solution. The two‐tier nature of the proposed architecture that balances centralized and distributed decision‐making ensures high scalability and adaptability to varying traffic conditions. Through experiments, proposed solution has demonstrated significantly improved results, thus, offers a robust and efficient solution for modern urban environments.
Bassma Aldahlan (Wed,) studied this question.