Learning-Based Fault-Tolerant Traffic Management Algorithms for Intelligent Transportation Systems

智能交通系统中基于学习的容错交通管理算法

基本信息

批准号：
1949710
负责人：
Kaan Ozbay
金额：
$ 29.99万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949710&HistoricalAwards=false
关键词：
Learning Based Fault Tolerant Traffic

项目摘要

This grant will support research on fault-tolerant intelligent transportation systems. Intelligent transportation systems have a strong potential for congestion mitigation and energy savings. However, such systems rely on a large number of sensors and communications devices deployed in complex environments. Therefore, these systems are prone to sensing faults (misreported, corrupted, or missing measurements), which can significantly compromise system efficiency. To address this challenge, this project will develop traffic management algorithms that detect and resolve sensing faults. Based on these algorithms, a paradigm of real-time, online fault-tolerant traffic management will be studied. This project will advance the knowledge about resiliency of intelligent transportation systems and provide ready-to-implement solutions. The solutions have the potential for mitigating the economic and environmental losses caused by sensing faults and improving cyber resilience of the traffic system. The expected results will provide hints on fault-tolerant design of other civil infrastructure systems as well. Graduate and undergraduate students from underrepresented groups will lead the research activities. Transportation agencies will be involved in discussion on concerns for actual deployment.The research activities jointly consider fault detection and resilient control for intelligent transportation systems. The modeling will synthesize physical law-based and learning-based approaches. Consequently, the models can integrate real-time information and adjust themselves to non-stationary environments with minimal human intervention. The objective will be approached via two research and one validation tasks. First, online fault detection and correction algorithms will be developed. These algorithms check consistency between observations from correlated sensors and between the observed data and physical law-based models, which contain prior empirical knowledge of the spatio-temporal correlation of sensor observations. Second, learning-based approaches will be incorporated into classical traffic control methods to design adaptive traffic management algorithms. The approach will provide guaranteed resiliency against sensing faults. Finally, the algorithms will be validated in a realistic micro-simulation testbed of a road network in New York City under a variety of sensing fault scenarios.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.

这笔赠款将支持容错智能交通系统的研究。智能交通系统在缓解拥堵和节能方面具有巨大潜力。然而，此类系统依赖于复杂环境中部署的大量传感器和通信设备。因此，这些系统很容易出现检测故障（误报、损坏或丢失测量），这会严重影响系统效率。为了应对这一挑战，该项目将开发检测和解决传感故障的交通管理算法。基于这些算法，将研究实时在线容错流量管理的范例。该项目将增进有关智能交通系统弹性的知识，并提供现成的解决方案。这些解决方案有可能减轻传感故障造成的经济和环境损失，并提高交通系统的网络弹性。预期结果也将为其他民用基础设施系统的容错设计提供提示。来自代表性不足群体的研究生和本科生将领导研究活动。运输机构将参与有关实际部署问题的讨论。研究活动共同考虑智能交通系统的故障检测和弹性控制。该建模将综合基于物理定律和基于学习的方法。因此，这些模型可以集成实时信息，并以最少的人为干预自行调整以适应非静态环境。该目标将通过两项研究和一项验证任务来实现。首先，将开发在线故障检测和纠正算法。这些算法检查相关传感器的观测值之间以及观测数据与基于物理定律的模型之间的一致性，其中包含传感器观测值时空相关性的先验知识。其次，基于学习的方法将被纳入经典交通控制方法中，以设计自适应交通管理算法。该方法将提供针对感知故障的有保证的弹性。最后，这些算法将在纽约市道路网络的真实微观模拟测试台上在各种传感故障场景下进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。