Transportation networks impact millions of people daily. Their efficiency immediately affects travel time, safety, and environmental sustainability. Unfortunately, various issues hinder the expected performance and efficiency of these networks. Traffic congestion is an up-to-date issue in the urban environment. Fuel consumption is high because travel time has increased, which has a passive environmental impact. Extensive research has been conducted to progress the intelligent transportation systems installed on communication networks and information to treat this congestion. However, there is a significant amount of affront residue in combining real-time data, estimation analytics, and 5G abilities effectively. This paper offers a novel routing algorithm integrating vehicular ad hoc networks with 5G technology to increase routing efficiency and minimize congestion. This routing is named 5G adaptive traffic management (5G-ATM). It collects real-time data from connected vehicles and roadside units to estimate traffic status and congestion. Out of simulations in an urban environment, the proposed 5G-ATM routing significantly progresses over previous routing protocols, such as an ant colony-inspired energy-efficient for optimized link state (AC-OLSR) routing and directional-cache agent-based location-aided (D-CALAR) routing. During rush hours, 5G-ATM shows the lowest traffic congestion events. Moreover, it minimizes average travel times by almost 8% compared to D-CALAR and 21% compared to AC-OLSR. These outcomes suggest that combining vehicular ad hoc networks with 5G technology helps manage traffic more efficiently, providing an efficient pathway and practical transportation systems.
