The rapid proliferation of Internet of Things (IoT) devices within intelligent transportation systems (ITS) has led to a massive influx of data, overwhelming existing cloud, fog, and edge computing models. This surge in data volume presents significant challenges, including increased latency, reduced scalability, and inefficiencies in real-time decision-making. Current methods often struggle to manage and process this data effectively, leading to delays and unreliable transportation services. This paper aims to tackle these challenges by introducing a novel approach that leverages agile optimization algorithms specifically designed for dynamic ride-sharing solutions. By integrating edge computing capabilities, these algorithms improve decision-making and data processing in real time significantly improving system responsiveness and efficiency. This approach minimizes latency, improves scalability, and delivers more reliable and effective transportation services. In addition, it explores future advancements and the potential integration of emerging technologies, such as 5G and artificial intelligence, to further bolster the capabilities of intelligent transportation systems. The goal of this research is to offer a complete solution to the pressing issues faced by modern ITS infrastructures.