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Traditional fixed-route public transit systems often struggle with inefficiency in suburban areas characterized by low population density and fluctuating demand. This paper presents and validates DynaRyde, a framework for optimizing suburban public transit by integrating dynamic route scheduling with on-demand passenger requests using well-established algorithms. Our approach utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to group spatially and temporally proximate ride requests, generating "virtual stops" in real time. Routes are then dynamically computed using a Traveling Salesman Problem (TSP)-like optimization heuristic, incorporating both mandatory transit hubs and virtual stops. We implemented and evaluated the DynaRyde system using the Simulation of Urban MObility (SUMO) traffic simulation environment on a realistic suburban road network under various demand scenarios. Performance was benchmarked against an enhanced fixed-route system and a non-clustering on-demand model. Results demonstrate that our integrated system significantly improves service quality and efficiency, achieving up to a 19% increase in service coverage and a 15–35% reduction in average waiting times compared to a resource-equivalent fixed-route baseline. These findings highlight the substantial potential of integrating on-demand clustering and dynamic routing strategies to create more adaptable and passenger-centric public transit solutions.