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

该项目为社区开发了一个网络-物理-社会系统，以激励新兴的乘车外包（如Uber和Lyft）和共享服务，以改善社会成果。其目标是实现新型的公私合作伙伴关系，利用这些服务为所有参与方带来双赢的结果：减少旅行时间，能源使用和排放，同时确保公共机构的成本效益;提高移动服务提供商的盈利能力;并改善所有旅行者的体验。研究成果将通过课程、开放源工具、期刊出版物、会议、讲习班和在线短期课程传播。该项目将为不同的学生群体提供跨学科培训，他们将成为下一代全球参与领导者的一部分。在与卡内基自然历史博物馆的合作下，将设计并向公众提供学习课程和实践活动。该项目为社区开发了一个理论，建模和计算框架，以激励新兴的移动服务，以实现效率和可靠性的全系统目标。这是通过关于出发时间、路线、汇集和限制（即，上下车地点），同时向移动服务提供商提供补贴，以换取有保证的系统改进。该项目推进了关于公共路权空间（如路缘石和道路）和出行需求如何定价和平衡以实现社会最优的基本知识。它开发了一种架构，将旅行者寻求最大限度地提高其效用和服务提供商提高服务效率和最大化收入的目标与基础设施和服务定价的新颖优化和控制相结合。此外，它还开发了高效和可扩展的算法，以估计和优化大规模网络的共享和个人车辆的混合流。该项目将评估多源高分辨率数据，包括来自移动服务提供商的车辆轨迹数据，以验证、测试和演示这一网络-物理-社会系统。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.