Multimodal Transportation Alliance Design With Endogenous Demand: Large-scale Optimization For Rapid Welfare Gains


We present an incentive-aligned collaborative pricing structure for competing urban transportation operators to jointly maximize system welfare. The alliance requires no change in infrastructure and captures behavior of all strategic agents in the system. We develop a two-stage SOS2 coordinate descent algorithm to solve the alliance design problem, which is formulated as a large-scale, nonconvex MINLP. We demonstrate the alliance’s utility on a full-scale case study of the Greater Boston Area which integrates 10 different datasets.

In preparation
Kayla Cummings
Kayla Cummings
PhD Candidate, Operations Research

Creative optimizer prioritizing societal impact.