Advances in networked communication systems have enabled the use of shared mobility forms including carsharing and ridesharing. Both government and private sectors engaged in planning shared mobility systems must choose between introducing new, shared mobility programs versus expanding existing ones, under uncertain budget and market demands. In this project, the PI will address the issue of demand uncertainty in design of shared mobility strategies considering shared fleet size, type, location design, and operational activities such as real-time vehicle routing, redistribution, and charging for carsharing systems; in addition to service region planning and shared mobility. The success of this project will: (i) advance both the theoretical and the computational frontiers of optimization methods for use in solving new transportation problems; and (ii) impact applications of shared mobility that relate to critical civil infrastructures, supply chain & logistics, and other service industries. Education plans, including promoting female and underrepresented minority groups in science, engineering, and management, will be collaboratively undertaken through the PIs' involvements in various education initiatives at the University of Michigan and Purdue University.
The objective of this collaborative research is to derive high-fidelity, data-driven mathematical models and provably efficient numerical algorithms that innovatively combine optimization and reinforcement learning for shared mobility system design and operations. In specific, we characterize shared mobility demand response as a multi-stage information revealing process, and abstract the corresponding decision process as sequential resource planning, allocation, and task prioritization, adjusted by varying decisions in later stages. We derive models and solution methods based on single-, two- and multi-stage stochastic optimization and dependent on full knowledge of demand distributions. We also investigate data-driven distributionally robust optimization methods and machine learning approaches to address ambiguous demand distributions and budget uncertainty. The derivation, validation, and calibration of this study aim at (i) formulating appropriate optimization-based models to characterize decision-data interdependence in complex shared mobility systems; (ii) deploying data-driven, distribution-free approaches for handling the distribution ambiguity of multi-sourced uncertainties in emerging shared mobility services; (iii) integrating learning approaches to dynamically adapt to endogenous system information and enhancing solutions from multi-stage optimization processes; (iv) designing efficient computational methods with solution quality guarantees to enable practical use of the models.
