Xiyuan Ren considered a ubiquitous data setting where we can fit choice models at the individual level (using inverse optimization-based machine learning), and showed how this can better capture joint travel choices within an activity scheduling context. We demonstrated this with data from Shanghai, where the agent level mixed logit model (fitted individual coefficients) outperforms state of the art dynamic discrete choice models. We also show that this can better integrate with optimization models to support choice-based system design. The work is now being applied to our C2SMART Center project in collaboration with Replica to estimate constrained mode choice models across population segments from their synthetic population data for New York State to design equitable statewide mobility services.

The research was funded by NSF CMMI-1652735 and the C2SMART Center (USDOT #69A3551747124).
Paper here: https://www.sciencedirect.com/science/article/pii/S0191261522001862

A new algorithm was developed to schedule both traditional and next-gen quay cranes at container terminals. It allows us to evaluate different levels of next gen quay crane investments for the Abu Dhabi Ports, which can reduce makespan by as much as 70% when all traditional cranes are replaced. Part of Omar Abou Kasm’s PhD dissertation work.

Paper: https://www.sciencedirect.com/science/article/pii/S0377221722008232