Collaborative Research: Uncertainty-Aware Multimodal Transit System Design with Shared Mobility under Ambiguous Rider Preferences

Project summary:

The objective of this project is to support research on multimodal transit services, combining fixed-route public transit with shared mobility services, while accounting for systematic uncertainties, rider preferences, and inherent complexity of different transportation modes. Car-less households face challenges accessing jobs and services mainly due to difficulty traveling between transit hubs and origins/destinations. At the same time, uncertainties in travel time, demand, and rider choices significantly impact design and operations of transit services. The research team investigates service planning and network design for fixed-route transit, as well as fleet sizing, routing, and relocation for shared mobility. Successful implementation is expected to (i) advance theories and computations in transportation and network problems under uncertainties, and (ii) enhance the potential of multimodal transit services to reduce private vehicle ownership, lower greenhouse gas emissions, and alleviate urban traffic congestion, while providing affordable transportation services for underserved groups. The team also contributes to curriculum development at the University of Minnesota and the University of Iowa, promotes diversity in STEM fields, enhances undergraduate research, and engages in K-12 outreach activities. The research focuses on developing a hierarchical, data-driven optimization framework that incorporates user behaviors for planning and operating multimodal transit systems under systematic uncertainties. Demand response to multimodal transit services is characterized through a hierarchical process to accommodate diverse user adoption preferences. Corresponding decision-making is modeled as sequential resource planning and allocation processes. The models and methodologies are based on stochastic optimization with single- and multi-stage dynamics. The primary outcomes include (1) an integrated hierarchical optimization framework to capture user behaviors; (2) data-driven methods to learn use preferences in transportation systems; (3) distribution-free approaches to accommodate unknown uncertainties in network design; and (4) efficient computational methods to enable practical application.