Principle Investigator: Joseph Chow
Transportation technologies have reached a point where they can no longer be evaluated as systems in isolation. Urban travel options have become increasingly abundant in today’s age of ubiquitous data and information and communications technologies: e.g. ridesharing, vehicle/bike sharing, and demand-responsive transit. Coordination of mobility solutions between multiple agencies during disasters and large scale incidents is increasingly more challenging. The solution in this study involves a careful reexamination of the supply-demand assignment models for transportation networks. It combines stable matching theory with network analysis for transformative impacts on transportation planning and game theory as applied to infrastructure systems. Policy-makers will be able to design cooperative policies with different transport operators, such as fare bundling, service leasing, asset co-location, to further encourage partnerships like that of Kansas City and Bridj. The same methodology can be used for risk pooling of resources in planning for disasters. The project will support training of a number of graduate, undergraduate, and K-12 STEM students from under-represented minorities in transportation systems and game theory. It will drive innovation and entrepreneurship by defining new functional roles in transportation planning that would engage with startup companies and incubators.
The study is split into two research thrusts. The first focuses on development of the core theory, which includes model formulation and algorithm development for traffic assignment as a many-to-one stable matching assignment game. Doing so would no longer assume that the transportation supply is fixed as in current methods. The model is then expanded to a many-to-many game to involve multiple operators so that cooperative policies between them can be evaluated. The second thrust focuses on applications of the theory, two of which are shared mobility systems and risk pooling strategies in disaster management. Customizations of the model to handle shared mobility services allow policymakers to evaluate specific shared mobility services like bike sharing or shared taxis. When the model is considered under uncertainty, cooperative strategies between multiple operators can be designed to pool risks together to minimize their impacts on individual operators. The study challenges over 60 years of research in transport modeling as an alternative theory of supply-demand interaction that considers both traveler and operator behavior.