Lyft tackles the complex challenge of matching drivers to riders in real-time using graph theory and optimization techniques. By modeling the problem as a bipartite graph, Lyft aims to maximize efficiency while adapting to dynamic urban conditions and demand fluctuations. The article discusses the mathematical foundations of matching problems and the practical considerations involved in dispatching within a ridesharing framework.