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

摘要提案：CMS 97-02612 PI：Srinivas Peeta，普渡大学 “职业生涯：大规模动态交通系统与信息系统的有效和鲁棒在线控制” 本研究解决了大规模交通系统的性能优化和有效的在线鲁棒控制作为一个实质性的例子，大规模的人造动态系统的容量限制和不同程度的不确定性的系统输入，其中流量必须路由从一个节点到另一个没有完整的先验信息，未来的条目/需求的系统。 目前缺乏这样的准确和详细的实时数据的状态下的交通系统已经排除了一个清晰的理解的动态现象，管理系统的性能，限制了发展的可行性和有效的实时控制策略的大型交通系统。 本研究将使用非线性自适应和鲁棒控制理论和神经网络开发一个新的领域的在线控制策略，寻求在一定的未来需求和网络状态条件下的大规模交通系统的性能优化。 这项工作将涉及理论，模拟和操作实验的混合。 与印第安纳州交通部合作，在一条主要的州际走廊上安装了先进的交通管理系统（ATMS），从该系统中获得实时数据。的运输和休斯运输管理系统将用于验证和评估所提出的模型和算法，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，特别是在先进传感器系统、在线数据提取和处理技术、鲁棒在线控制理论和高性能计算技术等领域，系统地弥合交通竞技场信息系统中不断发展的技术进步与现有课程中缺乏技术进步之间的差距。 本科生将参与实时数据访问和处理，并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。 ***

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. ***