A Dynamic Traffic Orchestration for Seamless Urban Mobility using Nexus Flow

City traffic congestion is a major bottleneck due to issues such as average commute times, increasing pollution, and economic challenges. This research proposes a traffic control system to enhance city transport utilizing the NexusFlow technology. The research bundles together various instruments and techniques to analyze GPS data and forecast the volume of vehicle traffic depending on factors like time of day of week, weather, holidays, etc. Thus, it is possible to characterize traffic trends and relationships based on various parameters. As the system operates algorithmically, the route optimization with NexusFlow builds an adaptive model to change lights, lanes, and routes over time to minimize congestion and travel duration proactively in real time. Data exchange is carried out for vehicles and infrastructure, and the system needs to take proactive steps. Cellular Vehicle to Everything (CV2X) gives an edge in traffic communication with other vehicles and different on-road elements. LiDAR offers optimal connectivity, providing passengers and roadway monitoring with real-time, instantly assessable data to ensure smoother flow in a traffic environment. Edge computing reduces delays in data transmission, which can be regarded as a force multiplier. Traffic control operations improve with faster models to arrive at a decision edge. Tests and trials show excellent results with real-life outcomes while measuring the scale and ensuring the system’s smooth operation. One concern, however, of this technology is the issue of privacy and security; it is necessary to implement a stringent privacy policy on stored data. The study presents a dynamic traffic control system with NexusFlow that is compliant with regulations. The results presented elevated communication accuracy between ${5 m}$ and ${3 m}$ GPS systems and reduced congestion for projected flow between 1200 vehicles $/ \mathrm{min}$ and 950 vehicles $/ \mathrm{min}$.