CAREER: Probabilistic Network Flow Theory: Embracing Emerging Big Data for Efficient, Reliable and Sustainable Multi-modal Transportation Systems

职业：概率网络流理论：拥抱新兴大数据，打造高效、可靠和可持续的多式联运系统

基本信息

批准号：
1751448
负责人：
Sean Qian
金额：
$ 50万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751448&HistoricalAwards=false
关键词：
CAREER Probabilistic Network Flow Theory

项目摘要

This Faculty Early Career Development Program (CAREER) project will establish a mathematical framework based on transportation system modeling and fusion of large-scale multi-source data across different systems including roadway, public transit and parking. The goal is to exploit the spatio-temporal characteristics of travel demand at different scales, understand how network disruption probabilistically affects the transportation systems, and help facilitate decision making regarding planning and real-time operations. The project will lay the foundation for data-driven transportation science that is to have an impact at all scales from individuals' quality of life to the nation's economy. This project will involve collaboration with several public agencies and private firms to develop, deploy and test real-world systems in the Pittsburgh metropolitan area based on large-scale data analytics. All models, algorithms, and examples will be implemented and open sourced in the public domain to promote access to applications and spark discussions from users all over the world. The results will be used to develop a new undergraduate course on data analytics for infrastructure management. Additionally, a virtual laboratory will be developed in conjunction with Carnegie Museum of Natural History for educating students in grades 7-12, college students, and the general public. Students from both Carnegie Mellon University and University of Pittsburgh will be engaged through learning sessions, data analytics competitions, and hands-on activities. The goal of this CAREER project is to develop theories and algorithms that utilize large-scale data to infer characteristics of probabilistic network flow and optimally manage transportation networks under uncertainty. High-dimensional joint probability distributions are used to explicitly model probabilistic network flow and system states in the context of flow dynamics and user behavior. Those joint probability distributions are learned, estimated, and predicted from fusing and mining fine-grained data collected over many years. They reveal the second-order statistics of flow and system states, namely variance-covariance, resulting in a better understanding of the inter-relations among flow/system characteristics, at high temporal and spatial granularity. This project will also develop rigorous statistical theories to identify recurrent and non-recurrent flow patterns in subnetworks through dynamic network partition. The science of network optimization will be advanced by integrating probabilistic network flow into decision making theories for both planning and operation. If successful, this research creates a new paradigm for sensing, modeling, designing, planning and operating complex infrastructure networks in an efficient, holistic, timely and reliable manner.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.

这个教师早期职业发展计划（职业）项目将建立一个数学框架，基于运输系统建模和融合大规模多源数据，包括道路，公共交通和停车位。目的是利用不同规模的旅行需求的时空特征，了解网络破坏如何概率地影响运输系统，并有助于促进有关计划和实时操作的决策。该项目将为数据驱动的运输科学奠定基础，该科学将对从个人的生活质量到国家经济产生影响。该项目将涉及与几家公共机构和私人公司的合作，以根据大规模数据分析在匹兹堡大都市地区开发，部署和测试现实世界系统。所有模型，算法和示例都将在公共领域实施和开放，以促进访问应用程序并引发全世界用户的讨论。结果将用于开发有关基础架构管理数据分析的新本科课程。此外，将与卡内基自然历史博物馆一起开发虚拟实验室，以教育7 - 12年级的学生，大学生和公众。来自卡内基·梅隆大学和匹兹堡大学的学生将通过学习会议，数据分析竞赛和动手活动来聘用。该职业项目的目的是开发利用大规模数据来推断概率网络流的特征并在不确定性下最佳管理运输网络的理论和算法。高维的联合概率分布用于在流动动态和用户行为的背景下明确模拟概率网络流动和系统状态。这些联合概率分布是通过收集的，从融合和采矿的细粒数据中学习，估计和预测的。他们揭示了流动和系统状态的二阶统计数据，即差异 - 协方差，从而更好地理解了在高时空和空间粒度下流量/系统特征之间的相互关系。该项目还将开发严格的统计理论，以通过动态网络分区来识别子网络中的经常性和非旋转流动模式。网络优化科学将通过将概率网络流动到计划和操作的决策理论中来提出。如果成功的话，这项研究将创建一个新的范式，以高效，整体，及时，及时和可靠的方式进行感应，建模，设计，计划和运营复杂的基础设施网络。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。