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

这个奖项的目的是调查一个典型的大都市地区高速公路运输系统的传感和控制方面的计算问题，该系统包括一个覆盖100平方英里的1,000车道英里网络，每天有1,000,000人使用，500,000辆汽车。讨论了在感知、状态估计、预测、控制和事件检测方面的技术挑战。传感需要实时处理和融合来自回路、摄像机、探测车和移动电话的数据。系统状态参数(例如，多个路段上的行程时间分布)的估计需要结合当前传感器数据、刚过去的数据以及历史数据，同时保持空间连续性约束。可以预测系统状态随时间和空间演变的多尺度模型将被开发出来，并用于计算可用“执行器”(可变信息标志、坡道仪表、信号)的实时控制动作，以提高性能。基于学习到的特征特征，相同的模型可以用来检测和分类伤害事故等事件，并具有足够的可靠性以触发适当的响应。这项研究的影响将是开发21世纪交通管理信息系统的核心能力。虽然当前的系统由各种传感器进行监控，并调度大量资源以防止系统崩溃，但在缺乏估计系统状态和预测系统行为的计算技术的情况下，运输经理和旅客都像蒙住眼睛一样做出决定。基于这项研究的系统可能会给交通管理人员和出行者带来巨大的好处。对于交通管理员，他们将提供系统性能的实时概述，并对各种控制选项的影响进行量化分析。对于旅行者，他们可以在计划和执行旅行期间提供实时信息和建议。该项目开发的技术还可能应用于其他问题，如空中交通管制，这些问题有许多基本特征。

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.***