Differentiable Optimization for On-Demand Transportation Systems

Abstract

We propose a data-driven approach to on-demand transportation routing as a linear program with rider pickup times as parameters, using differentiable optimization to compute gradients of some cost with respect to these parameters. This solution allows for routing optimization in real-time and is a significant improvement in efficiency to the mixed-integer formulation.