US Ignite: Collaborative Research: Focus Area 1: Social Computing Platform for Multi-Modal Transit

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This project addresses the problem of urban transportation and congestion by directly engaging individual commuters. Because of the widespread use of smart devices, users are modeled as active agents in a shared economy, with algorithms designed to incentivize them to take actions that are efficient for the overall transportation system. Many commercially available Internet of Things solutions for multimodal transit focus on what is best for each individual from his or her local perspective. As the number of these local solutions grows, the misalignment between objectives of individual and the overall system also grows. An information bottleneck also forms, since massive data is collected by municipalities and users, but neither has the resources to develop real-time analytics and controls. Currently, very little has been done to provide an overarching solution that balances the needs of multiple parties, including commercial companies, municipal service providers, and individuals. The project will configure a computing and information sharing platform that overcomes the incentive gap between individuals and municipalities. This platform offers mixed-mode routing suggestions and general system information to travelers and in turn provides service providers with high-fidelity information about how users are consuming transportation resources. The platform also help to improve community engagement in policy and regulatory decisions by serving as a virtual commons where individual citizens can connect to municipal service providers. The platform will be suitable for application to any smart city and will be tested in Seattle, WA and Nashville, TN.The research agenda divides into three key thrusts: 1) a hierarchical optimization architecture amenable to implementation on a distributed platform; 2) a mechanism design framework for recruiting resources from strategic users and incentivizing mixed-mode routes; 3) a software defined networking supported social computing platform that utilizes edge devices for computation. The proposed research extends existing optimization techniques for solving the multimodal transit problem by incorporating probabilistic representations of events, creating a near-optimal distributed algorithm by employing submodularity, and folding in incentive mechanisms into the optimization problem. In addition, the results will significantly advance the theory of mechanism design by developing novel adaptive contracting and incentive mechanisms in a societal setting. Real world experimental trials will be conducted with the support of municipal and industry partners to validate the platform and supporting algorithms.