Optimization-driven Demand Prediction Framework for Suburban Dynamic Demand-Responsive Transport Systems

Abstract

Demand-Responsive Transport (DRT) has grown over the last decade as an ecological solution to both metropolitan and suburban areas. It provides a more efficient public transport service in metropolitan areas and satisfies the mobility needs in sparse and heterogeneous suburban areas. Traditionally, DRT operators build the plannings of their drivers by relying on myopic insertion heuristics that do not take into account the dynamic nature of such a service. We thus investigate in this work the potential of a Demand Prediction Framework used specifically to build more flexible routes within a Dynamic Dial-a-Ride Problem (DaRP) solver. We show how to obtain a Machine Learning forecasting model that is explicitly designed for optimization purposes. The prediction task is further complicated by the fact that the historical dataset is significantly sparse. We finally show how the predicted travel requests can be integrated within an optimization scheme in order to compute better plannings at the start of the day. Numerical results support the fact that, despite the data sparsity challenge as well as the optimization-driven constraints that result from the DaRP model, such a look-ahead approach can improve up to 3.5% the average insertion rate of an actual DRT service.