Collaborative Research: Optimal Sensor Selection and Robust Traffic Detection and Estimation in a World of Connected Vehicles

可靠的交通管理策略需要准确地了解道路上的交通水平。尽管互联汽车(CV)的出现为通过无线通信共享车辆位置和速度的交通数据提供了巨大的潜力，但也存在隐私问题和带宽限制-并不是所有用户都想共享，也不是所有车辆都能够共享。该项目将通过设计方法来解决这两个问题，方法是指导选择一些道路用户进行数据共享和分析，以提供实时准确的交通水平估计，同时解决隐私和带宽问题。在整个项目中，交通和机器学习科学方面的培训模块将在德克萨斯大学圣安东尼奥分校和德克萨斯大学奥斯汀分校设计，并将在UTSA招收来自代表性不足群体的学生，那里58%的在校生是少数民族。该项目将：(I)考虑用户数据的隐私，从而保持车辆和用户的匿名性；(Ii)识别交通状况因事故而发生的突然变化；(Iii)设计从简历实时收集的交通数据的时变选择；以及(Iv)量化网络带宽的限制以及交通状况和道路属性的不确定性。该项目的主要贡献在于推动了简历作为实时、移动交通传感器的使用。这涉及到来自多个学科的概念的集成：交通流、网络系统、估计和机器学习理论。具体地说，该项目将研究可计算可扩展的方法，交通运营商可以使用这些方法来优化从CV中采样数据，同时满足隐私和带宽限制，从而实时监控流量。理论基础将通过与奥斯汀和圣安东尼奥两个城市的合作，通过现实的交通设置来验证。这项研究的更广泛影响超越了交通网络：计算算法将适用于涉及网络偏微分方程组和移动传感平台的相关问题，如机器人和无人机的环境监测。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Reliable traffic management strategies require accurate knowledge of traffic levels on roads. Though the emergence of connected vehicles (CV) offers tremendous potential for sharing traffic data about vehicles' locations and speeds through wireless communications, there are both privacy concerns and bandwidth constraints - not all users want to share and not all vehicles are able to share. This project will address both issues by designing methods to guide the selection of some road users for data sharing and analysis to provide accurate estimation of traffic levels in real time, while addressing privacy and bandwidth issues. Throughout this project, training modules in traffic and machine learning sciences will be designed at both UT San Antonio and UT Austin and students from underrepresented groups will be recruited at UTSA where 58 percent of enrolled students are minorities.The project will: (i) consider privacy of user data hence maintaining anonymity of vehicles and users, (ii) identify sudden changes in traffic conditions due to accidents, (iii) design a time-varying selection of traffic data collected in real-time from CVs, and (iv) quantify limits on the network bandwidth and uncertainty in traffic conditions and road properties. The project's major contribution lies in advancing the use of CVs as real-time, mobile traffic sensors. This involves the integration of concepts from multiple disciplines: traffic flow, networked systems, estimation, and machine learning theories. Specifically, the project will investigate computationally scalable methods that traffic operators can utilize to optimally sample data from CVs while satisfying privacy and bandwidth constraints, thereby monitoring traffic in real-time. The theoretical foundations will be validated with realistic traffic setups through collaborations with the cities of Austin and San Antonio. The broader impact of the research transcends traffic networks: the computational algorithms will be applicable to related problems involving networked systems of partial differential equations and moving sensing platforms such as environmental monitoring by robot and unmanned aerial vehicles.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.