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，研究人员专注于为未来的“智能”大都市交通管理设计实时网络传感和驱动平台的挑战，目的是同时减少拥堵，污染和旅行者延误。今天，大多数城市的交通控制是基本的：在较小的城市，许多交通信号仍然是孤立的，虽然大多数较大的城市有集成的信号系统，但它们在大多数情况下，并没有动态地响应实时车辆信息。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瞬时状况。通信、导航和传感器技术的最新进展为提高大城市街道的智能化和效率提供了比目前更多的机会。拟议中的绿色城市智能交通体系结构的关键要素是能够在交通/污染传感和交通控制之间实现“闭环”;通过中央交通管理部门和驾驶员之间的激励合作实现的系统。在这一合作中，“智能”交通信号灯、导航服务器和车载导航仪发挥了关键作用。未来的交通信号系统除了传统的控制信号交叉口的车流量外，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），并向船上的导航员广播交通规则、路线和限制。导航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，车载导航仪，激励的拥堵/污染费和/或“良好的导航”信用，提出最佳路线的基础上，司机的喜好，当地的感知交通，和信号灯的时间。所有这一切都是通过有效的车辆到道路基础设施的通信实现的，从3G通道到DSRC无线电（路边和车载），这些无线电能够在各种架构组件之间进行实时、低成本、可扩展的信息交换。更广泛的影响：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教师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，仿真和实验来证明。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.