A Graph-Based Heuristic for Bus Route Planning Using Taxi Origin-Destination Data

As the global population ages and transportation resources become increasingly constrained, mid-sized suburban aging cities face growing challenges in designing efficient and sustainable public transit systems. In response, this study introduces a comprehensive data-driven heuristic based on graph theory for bus stop selection and route planning, leveraging large-scale taxi origin-destination data. The proposed algorithm features a density-based spatial clustering module that respects pedestrian networks and physical distances to identify candidate bus stops corresponding to mobility hotspots. Subsequently, a K-medoids clustering component based on road distances further segments and assigns bus stops to bus routes while respecting road networks. Finally, a composite Hamiltonian path construction heuristic plans feasible bus routes for each segment. A case study of Susono city in Shizuoka, Japan, demonstrates the effectiveness of the proposed methodology in generating four practical, user-centric, and context-aware bus routes with high operational efficiency. Overall, the proposed algorithm provides a data-driven framework for effective and adaptive mobility planning suitable for both local authorities and bus operators.