Study on multi-vehicle routing problem using clustering method for demand responsive transit

Abstract

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has been the goal of many researches over the past decades. In many studies, routing algorithms for DRT systems have been proposed and developed. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method (DDPS) considering the imbalance of users’ origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random case, concentration case, and directed case. The result shows that the proposed DDPS method decreases the drop of service ratio due to an increase in demand density and computation time compared to other mixed-integer programming-based algorithms. In addition, compared to other clustering-based algorithm, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden. This can be further improved by additional adjustments of the constraints given to the routing algorithm process.