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

US Ignite：合作研究：重点领域 1：多式联运社交计算平台

基本信息

批准号：
1646912
负责人：
Baosen Zhang
金额：
$ 30万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-10-01 至 2019-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646912&HistoricalAwards=false
关键词：
US Ignite Collaborative Research Focus

项目摘要

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.

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主动代理，算法旨在激励他们采取对整个交通系统有效的行动。许多商业上可用的多式联运物联网解决方案都侧重于从个人的角度出发，为每个人提供最佳服务。随着这些局部解决方案数量的增加，个人目标与整个系统目标之间的不一致也会增加。信息瓶颈也形成了，因为市政当局和用户收集了大量数据，但都没有资源来开发实时分析和控制。目前，在提供平衡多方需求的总体解决方案方面做得很少，包括商业公司、市政服务提供商和个人。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。该平台为旅行者提供混合模式的路线建议和一般系统信息，并反过来为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。该平台还有助于改善社区参与政策和监管决策，作为一个虚拟公域，公民个人可以与市政服务提供者联系。该平台将适用于任何智慧城市的应用，并将在华盛顿州的西雅图和田纳西州的纳什维尔进行测试。研究议程分为三个关键目标：1）适合在分布式平台上实现的分层优化架构; 2）从战略用户招募资源和激励混合模式路由的机制设计框架; 3）软件定义的网络支持的社交计算平台，其利用边缘设备进行计算。拟议的研究扩展了现有的优化技术，解决多式联运问题，将事件的概率表示，创建一个接近最优的分布式算法，采用子模块化，并折叠在激励机制的优化问题。此外，研究结果将显着推进机制设计的理论，通过开发新的适应性合同和激励机制，在社会环境。将在市政和行业合作伙伴的支持下进行真实的世界实验性试验，以验证平台和支持算法。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Uncertainty in Multicommodity Routing Networks: When Does It Help?

DOI：
10.1109/tac.2019.2962102
发表时间：
2020-11
期刊：
IEEE Transactions on Automatic Control
影响因子：
6.8
作者：
S. Sekar;Liyuan Zheng;L. Ratliff;Baosen Zhang
通讯作者：
S. Sekar;Liyuan Zheng;L. Ratliff;Baosen Zhang

Data Driven Spatio-Temporal Modeling of Parking Demand

数据驱动的停车需求时空建模

DOI：
发表时间：
2018
期刊：
American Control Conference
影响因子：
0
作者：
Fiez, Tanner;Ratliff, Lillian;Dowling, Chase;Zhang, Baosen
通讯作者：
Zhang, Baosen

Optimizing Curbside Parking Resources Subject to Congestion Constraints

根据拥堵限制优化路边停车资源

DOI：
发表时间：
2017
期刊：
including the Symposium on Adaptive Processes
影响因子：
0
作者：
Dowling, Chase;Fiez, Tanner;Ratliff, Lillian;Zhang, Baosen
通讯作者：
Zhang, Baosen

Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles

用于优化固定时间表交通车辆准点性能的无监督机制

DOI：
10.1109/smartcomp.2017.7947057
发表时间：
2017
期刊：
Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles
影响因子：
0
作者：
Sun, Fangzhou;Samal, Chinmaya;White, Jules;Dubey, Abhishek
通讯作者：
Dubey, Abhishek

SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation