CPS: Small: Collaborative Research: Optimal Ride Service For All: Users, Service Providers and Society

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This project develops a cyber-physical-social system for communities to incentivize emerging ride-sourcing (such as Uber and Lyft) and sharing services to improve societal outcomes. The goal is to enable novel public-private partnerships that leverage these services for a win-win-win outcome for all parties involved: reducing travel times, energy use and emissions, while ensuring cost-effectiveness for public agencies; boosting mobility service providers' profitability; and improving the experience of all travelers. Research results will be disseminated through courses, open-source tools, journal publications, conferences, workshops, and an online short course. This project will provide interdisciplinary training to a diverse group of students, who will be part of the next generation of globally-engaged leaders. Learning sessions and hands-on activities will be designed and offered to general public under the collaboration with the Carnegie Museum of Natural History. This project develops a theoretical, modeling, and computational framework for communities to incentivize emerging mobility services to achieve system-wide goals on efficiency and reliability. This is done through optimally pricing a surcharge or credit to riders' fare with respect to departure times, routes, pooling and curbs (i.e., pick-up/drop-off locations), in conjunction with subsidies to mobility service providers in exchange for guaranteed system improvement. This project advances fundamental knowledge regarding how public right-of-way spaces (such as curbs and roads) and travel demand should be priced and balanced for social optimum. It develops an architecture that integrates travelers' seeking to maximize their utilities and service providers' goals for improving service efficiency and maximizing revenue, with novel optimization and controls of infrastructure and service pricing. In addition, it develops efficient and scalable algorithms to estimate and optimize mixed flows of shared and personal vehicles for large-scale networks. This project will assess multi-source high-resolution data, including vehicle trajectory data from mobility service providers to validate, test and demonstrate this cyber-physical-social system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.