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

CPS：小型：协作研究：为所有人提供最佳乘车服务：用户、服务提供商和社会

基本信息

批准号：
1931794
负责人：
Bo Zeng
金额：
$ 14.5万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931794&HistoricalAwards=false
关键词：
CPS Small Collaborative Research Optimal

项目摘要

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.

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（如优步和Lyft）和共享服务，以改善社会成果。目标是建立新的公私合作伙伴关系，利用这些服务为所有相关方带来三赢的结果：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；改善所有旅客的体验。研究成果将通过课程、开源工具、期刊出版物、会议、研讨会和在线短期课程进行传播。该项目将为多样化的学生群体提供跨学科培训，这些学生将成为下一代全球领导者的一部分。与卡内基自然历史博物馆合作，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方面的全系统目标。这是通过根据出发时间、路线、拼车和限制（即上下车地点）对乘客的票价进行最优定价来实现的，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。该项目推进了关于如何对公共路权空间（如路缘和道路）和出行需求进行定价和平衡以实现社会最优的基础知识。它开发了一种架构，将旅行者追求效用最大化的目标与服务提供商提高服务效率和收入最大化的目标结合起来，并对基础设施和服务定价进行了新颖的优化和控制。此外，它还开发了高效和可扩展的算法来估计和优化大规模网络中共享和个人车辆的混合流量。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。该奖项反映了美国国家科学基金会的法定使命，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。