CAREER: Efficient and Robust On-Line Control of Large-Scale Dynamic Traffic Systems with Information Systems

摘要建议：CMS 97-02612 PI：SRinivas Peeta，普渡大学“CARISE：Efficient and Robust Online Control of Large Scaled Dynamic Communications Systems with Information Systems”这项研究将大规模交通系统的性能优化和有效的在线鲁棒控制作为具有容量约束和系统输入的不同程度不确定性的大规模人造动态系统的实质性例子，其中必须在没有关于系统未来进入/需求的完整先验信息的情况下将流量从一个节点在线路由到另一个节点。目前缺乏关于交通系统状态的这种准确和详细的实时数据，这使得人们无法清楚地了解支配系统性能的动态现象，限制了针对大型交通系统的可行和有效的实时控制策略的发展。本研究将利用非线性自适应与鲁棒控制理论与神经网络，开发一种新的在线控制策略，寻求在一定的未来需求和网络状态条件下，大规模交通系统的性能优化。这项工作将涉及理论、模拟和操作实验的结合。从最近安装在一条主要州际走廊上的先进交通管理系统(ATM)获得的实时数据，这是与印第安纳州交通部正在进行的合作。交通部和休斯运输管理系统将用于验证和评估建议的模型和算法，从而提高现有道路网络的性能效率。教育目标是通过教学和实验，系统地弥合交通领域信息系统不断发展的技术进步与现有课程中的缺失之间的差距，特别是在先进传感器系统、在线数据提取和处理技术、稳健的在线控制理论和高性能计算技术等领域。本科生将参与实时数据访问和处理，并将强调专业发展和扩展课程发展的计划包括在同伴教育计划中。***

Abstract Proposal: CMS 97-02612 PI: Srinivas Peeta, Purdue University "Career: Efficient and Robust On-Line Control of Large- Scale Dynamic Traffic Systems with Information Systems" " This research addresses the performance optimization and the efficient on-line robust control of large-scale traffic systems as a substantive example of large-scale man-made dynamic systems with capacity constraints and different degrees of uncertainties in system inputs, in which flows must be routed on-line from one node to another without complete a priori information on future entries/demands to the system. The current lack of such accurate and detailed real-time data on the state of the traffic system has precluded a clear understanding of the dynamic phenomena that govern system performance, limiting the development of feasible and effective real-time control strategies for large traffic systems. This research will use nonlinear adaptive and robust control theory and neural networks to develop a new realm of on-line control strategies that seek the performance optimization of large-scale traffic systems under certain future demands and network state conditions. The work will involve a blend of theory, simulation, and operational experiments. Real-time data obtained from a recently installed advanced traffic management system (ATMS) on a major interstate corridor in an on-going collaborative effort with the Indiana Dept. of Transportation and Hughes Transportation Management Systems will be used to validate and evaluate the proposed models and algorithms, leading to an enhanced performance efficiency of existing road networks. The education objectives are to systematically bridge the gap between evolving technological advances in information systems in the transportation arena and their absence from existing curriculum through teaching and experimentation, specifically in the areas of advanced sensor systems, on- line data extraction and processing techniques, robust on- line control theory, and high performance computational techniques. Undergraduate students will be involved with real-time data accessing and processing, and programs accenting professional development and extension course development are included in the companion educational plan. ***