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

• 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

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.

该补助金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约能源方面具有很大的潜力。然而，这样的系统依赖于部署在复杂环境中的大量传感器和通信设备。因此，这些系统容易出现感测故障（误报、损坏或缺失测量），这可能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流量管理算法。基于这些算法，实时，在线容错流量管理的范例将进行研究。该项目将推进有关智能交通系统弹性的知识，并提供随时可实施的解决方案。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。预期的结果将提供其他民用基础设施系统的容错设计以及提示。来自代表性不足群体的研究生和本科生将领导研究活动。交通机构将参与讨论实际部署的问题。研究活动共同考虑智能交通系统的故障检测和弹性控制。建模将综合基于物理定律和基于学习的方法。因此，这些模型可以整合实时信息，并在最少人为干预的情况下调整自己以适应非平稳环境。将通过两项研究和一项验证任务来实现这一目标。首先，将开发在线故障检测和校正算法。这些算法检查相关传感器的观测之间的一致性和观测数据和基于物理定律的模型之间的一致性，其中包含传感器观测的时空相关性的先验经验知识。第二，将基于学习的方法纳入经典的交通控制方法，设计自适应交通管理算法。该方法将提供针对感测故障的有保证的弹性。最后，该算法将在纽约市的道路网络在各种传感故障scenaries.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持下，一个现实的微观仿真测试平台进行验证。

项目成果

Resilience of Dynamic Routing in the Face of Recurrent and Random Sensing Faults

动态路由面对反复出现和随机的感知故障时的弹性

• DOI: 10.23919/acc45564.2020.9147588
• 发表时间: 2020
• 期刊: 2020 American Control Conference (ACC
• 作者: Xie, Qian;Jin, Li
• 通讯作者: Jin, Li

Security Risk Analysis of the Shorter-Queue Routing Policy for Two Symmetric Servers

两台对称服务器短队列路由策略的安全风险分析

• DOI: 10.23919/acc45564.2020.9147745
• 发表时间: 2020
• 期刊: 2020 American Control Conference (ACC
• 作者: Tang, Yu;Wen, Yining;Jin, Li
• 通讯作者: Jin, Li