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. ***
