Operational route planning under uncertainty for Demand Adaptive Systems

With an increasing need for more flexible mobility services, we consider an operational problem arising in the planning of Demand Adaptive Systems (DAS). Motivated by the decision of whether to accept or reject passenger requests in real time in a DAS, we introduce the operational route planning problem of DASs. To this end, we propose an algorithmic framework that allows an operator to plan which passengers to serve in a DAS in real-time. To do so, we model the operational route planning problem as a Markov decision process (MDP) and utilize a rolling horizon approach to approximate the MDP via a two-stage stochastic program in each timestep to decide on the next action. Furthermore, we determine the deterministic equivalent of our approximation through sample-based approximation. This allows us to decompose the deterministic equivalent of our two-stage stochastic program into several full information planning problems, which can be solved in parallel efficiently. Additionally, we propose a consensus-based heuristic and a myopic approach. We perform extensive numerical studies based on real-world data provided to us by the public transportation provider of Munich, Germany. We show that our exact decomposition yields the best results in under five seconds, and our heuristic approach reduces the serial computation time by 17 - 57% compared to our exact decomposition, with a solution quality decline of less than one percent. From a managerial perspective, we show that by switching a fixed-line bus route to a DAS, an operator can increase profit by up to 49% and the number of served passengers by up to 35% while only increasing the travel distance of the bus by 14%. Furthermore, we show that an operator can reduce their cost per passenger by 43 - 51% by increasing route flexibility and that incentivizing passengers to walk slightly longer distances reduces the cost per passenger by 83-85%.