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

该项目通过直接吸引个人通勤者来解决城市交通和拥堵问题。由于智能设备的广泛使用，用户被建模为共享经济中的活跃主体，其算法旨在激励他们采取对整个交通系统有效的行动。许多商用的多式联运物联网解决方案都是从每个人的角度出发，关注什么对每个人最有利。随着这些局部解决方案数量的增长，个人和整个系统的目标之间的不一致也在增长。由于大量数据是由市政当局和用户收集的，但两者都没有资源来开发实时分析和控制，因此也形成了信息瓶颈。目前，在提供平衡多方需求的总体解决方案（包括商业公司、市政服务提供商和个人）方面做得很少。该项目将配置一个计算和信息共享平台，以克服个人和市政当局之间的激励差距。该平台向旅行者提供混合模式路线建议和一般系统信息，进而向服务提供商提供有关用户如何消耗交通资源的高保真信息。该平台还作为虚拟公地，公民个人可以与市政服务提供商建立联系，从而有助于提高社区对政策和监管决策的参与。该平台将适用于任何智慧城市，并将在华盛顿州西雅图和田纳西州纳什维尔进行测试。研究议程分为三个关键重点：1)适合在分布式平台上实施的分层优化架构；2)战略用户资源招募和混合模式路线激励机制设计框架；3)利用边缘设备进行计算的软件定义网络支持的社会计算平台。本文提出的研究扩展了现有的解决多模式交通问题的优化技术，将事件的概率表示纳入其中，通过采用子模块化创建一个接近最优的分布式算法，并将激励机制纳入优化问题。此外，研究结果将通过在社会环境中发展新的适应性契约和激励机制，显著推进机制设计理论。在市政和行业合作伙伴的支持下，将进行真实世界的实验试验，以验证平台和支持算法。

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.