KDI: Adaptive Sensing and Control of Large Systems Under Uncertainty with Application to Metropolitan-Area Freeways

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9873086MalikThe purpose of this award is to investigate the computational problems in sensing and control of a prototypical metropolitan area freeway transportation system, comprising a 1,000 lane mile network spread ver 100 square miles, used daily by 1,000,000 people in 500,000 vehicles. Technical challenges in sensing, state estimation, prediction, control and incident detection are addressed. Sensing requires real-time processing and fusing of data from loops, video cameras, probe vehicles, and cellular phones. Estimation of system state parameters (e.g. travel time distributions over numerous links) requires combining current sensor data, data from the immediate past, as well as historical data while maintaining spatial continuity constraints. Multiple scale models that can predict the evolution of system state over time and space will be developed and used to calculate real-time control actions for the available "actuators" (variable messages signs, ramp meters, signals) in order to improve performance. Based on learned characteristic signatures, the same models can be used to detect and classify incidents such as injury accidents with sufficient reliability to trigger appropriate responses.The impact of this research will be to develop the core capabilities of a traffic management information system for the twenty-first century. While the current system is monitored by a variety of sensors and vast resources are dispatched to prevent system breakdown, in the absence of computational technology to estimate system state and to predict system behavior, both transportation managers and travelers make decisions as if they were blind-folded. Systems based on this research potentially offer huge benefits to both traffic managers and travelers. For traffic managers, they will provide a real-time overview of system performance and a quantitative analysis of the impact of various control options. For travelers, they can provide real-time information and advice during the planning and execution of their trips. The techniques developed in the project also potentially have application to other problems, such as air traffic control, which share many of its essential characteristics.***