NeTS:Large: Collaborative Research: Closing the loop between traffic/pollution sensing and vehicle route control using traffic lights and navigators.

在这个项目中，研究人员专注于设计一个实时网络传感和驱动平台的挑战，以期同时减少拥堵、污染和出行者延误。今天，大多数城市的交通控制都是初级的：在较小的城市中，许多交通信号仍然是孤立的，尽管大多数较大的城市都有集成的信号系统，但它们在很大程度上并不是根据实时车辆信息动态计时的。拥堵费作为一种交通管理和创收工具越来越受欢迎，它通常基于历史交通数据，而不是动态变化以反映即时情况。通信、导航和传感器技术的最新进步提供了比今天更多的机会来提高大都市街道的智能和效率。拟议的绿色城市智能交通体系结构的关键要素将是交通/污染传感和交通控制之间的“闭合环路”的能力；这个系统是通过中央交通管理和司机之间的激励合作实现的。在这种协作中，智能交通信号灯、导航器服务器和车载导航器扮演着关键角色。除了传统的信号控制交叉口的车流控制外，未来的交通信号系统还将感知交通特征和车辆排放，从车辆传感器收集数据(污染、排放、位置等)，并向车载导航器广播交通建议、路线和限制。导航器服务器与中央交通控制器交互，并向车载导航器建议最佳路线。最后，在拥堵/污染费用和/或良好导航信用的激励下，车载导航员根据司机的偏好、当地感知的交通和信号配时提出最优路线。所有这一切都是由高效的车辆到道路基础设施通信实现的，从3G频道到DSRC无线电(路边和车载)，使各种建筑组件之间能够进行实时、低成本、可扩展的信息交换。广泛的影响：该项目将是高度跨学科的；它将受益于计算机科学、大气科学和城市规划学院和学生的合作和专业知识。我们的解决方案的有效性将通过模拟、仿真和实验来验证。该项目的多学科性质将带来新的教育机会。

In this project, the researchers focus on the challenges of designing a real-time networked sensing and actuation platform for future 'intelligent' metropolitan traffic management with the aim of simultaneously reducing congestion, pollution, and traveler delays. Today, most urban traffic control is rudimentary: in smaller cities many traffic signals remain isolated, and while most larger cities have integrated systems of signals, they for the most part, are not dynamically timed in response to real-time vehicle information. Congestion fees, which are increasingly popular as a traffic management and revenue-generating tool, are usually based on historical traffic data rather than varying dynamically to reflect instantaneous conditions. Recent advances in communication, navigation, and sensor technologies present far more opportunities to increase the intelligence and efficiency of metropolitan streets than are in place today.The pivotal element of the proposed Green City intelligent transport architecture will be the ability to 'close the loop' between traffic/pollution sensing and traffic control; a system achieved through an incentivized collaboration between the central traffic management and the drivers. In this collaboration, the 'intelligent' traffic signals, the Navigator Server and the on-board navigators play key roles. In addition to the traditional control of vehicular flow at signalized intersections, future traffic signal systems will sense traffic characteristics and vehicular emissions, collect data from vehicle sensors (pollution, emission, position, etc.), and broadcast traffic advisories, routings, and restrictions to on-board navigators. The Navigator Server interacts with central traffic controllers, and proposes optimal routes to the on-board navigators. Finally, the on-board navigators, incentivized by congestion/pollution fees and/or 'good navigation' credits, propose optimal routings based on drivers' preferences, local perceived traffic, and signal timing. All this is enabled by efficient vehicle to roadway infrastructure communications, from 3G channels to DSRC radios (roadside and on-board) that enable real-time, low cost, scalable information exchanges among the various architecture components.Broader Impact: This project will be highly interdisciplinary; it will benefit from the collaboration and expertise of computer science, atmospheric science, and urban planning faculty and students. The efficacy of our solution will be demonstrated via simulation, emulation, and experimentation. New education opportunities will result from the multidisciplinary nature of the project.