This thesis explores the application of dynamic graph theory to optimize urban ridesharing platforms, particularly during peak-hour traffic congestion. By integrating real-time traffic data with dynamic routing algorithms, this research aims to improve ride-sharing efficiency, reduce congestion, and enhance urban mobility. The study investigates the effectiveness of combining Dijkstra’s and A* algorithms, focusing on the dynamic adjustment of routes based on real-time traffic conditions. 

Further, the study leverages a simulation of the city of Gothenburg, Sweden’s road network, this the result from this demonstrates that the combination of these algorithms significantly reduces travel time and congestion compared to traditional static routing methods. The results of this study contribute to the development of more efficient, adaptive, and scalable ride-sharing systems, with implications for urban transportation planning and policy. 

The findings emphasize the importance of integrating real-time traffic data into ride-sharing platforms to improve service delivery and reduce congestion during peak hours.