Dynamic Clustering Meeting Points Strategy to Improve Operational Service Capability of Flex-Route Transit

Abstract

To improve the operational service capability of flex-route transit under the condition of uncertain and inhomogeneous passenger travel demand, a dynamic meeting points optimization solution is proposed. This solution can set dynamic pickup and drop-off meeting points according to real-time travel demand. The flex-route transit operation mode is described in detail and is divided into a two-stage optimization problem. Mixed integer programming is employed to formulate the problem with a twofold objective—(1) serve as many requests as possible; and (2) minimize the time cost of the accepted passengers. An improved memetic solution algorithm is proposed for the model constructed. Computational results based on real-world cases show that the dynamic clustering meeting points solution breaks the space constraints of the original pickup and drop-off points of passengers and can significantly reduce the proportion of passenger reservation requests rejected without increasing operation cost.