NetSE: Small: Collaborative Research: Integrating Real Time Traffic Signal Control with Networking Control Strategies to Optimize Urban Traffic Networks

利用实时网络技术对城市交通信号进行优化，可以显著提高交通系统的性能。该项目的目标是应用通信技术和通信网络技术来控制交通信号和车辆。这是一个跨学科的建议，它将联合收割机结合在交通工程中使用的基于测量数据中的不确定性的优化技术，与在通信网络中使用的基于实时测量和数据传播的分布式控制策略。研究工作被组织成一个有序的进程，使用越来越多的信息和处理复杂性来确定程序的增量价值。我们将从优化孤立交通信号处的流量开始，然后逐步优化干线上的流量，最后优化整个交通网络中的流量。首先，我们将根据实时流量信息控制交通信号，然后使用信号控制车辆的路径，使用偏转路由技术，最后执行单个车辆的路径规划。我们将使用聚类技术和信息减少技术，如鱼眼路由，正在开发的自组织网络，规模技术，适用于小型网络的交通网络在大城市地区。理解和影响流量控制的挑战在网络流，通信，优化和统计建模方面开辟了新的研究课题。所有程序都将使用来自曼哈顿选定地区的真实的数据进行测试。该项目的更广泛影响包括：（1）通过减少在交通信号灯处花费的时间，减少燃料消耗和通勤时间;（2）将在两个互补领域之间建立合作，其目标相似，即增加吞吐量和优化网络流量;（三）它将通过开发学习模块和鼓励少数民族学生参与这一活动，积极吸引研究生和本科生参与。跨学科的努力。这项工作将广泛传播给运输和网络社区。

Utilizing real-time networking techniques to optimize urban traffic signals can significantly improve the transportation system performance. The objective of this project is to apply communications technologies and communications networking techniques to control both traffic signals and vehicles. This is an interdisciplinary proposal that will combine the optimization techniques based on uncertainties in the measured data that are used in traffic engineering, with distributed control strategies, based on real-time measurement and data dissemination that are used in communication networks. The research effort is organized into an orderly progression that uses increasing amounts of information and processing complexity to determine the incremental value of the procedures. We will start by optimizing the flows at isolated traffic signals, then progress to flows on arterials, and finally to flows in the entire traffic network. Initially we will control the traffic signals based on real-time flow information, then progress to using the signals to control the paths of vehicles, using deflection routing techniques, and finally perform route planning for individual vehicles. We will use clustering techniques and information reduction techniques, such as fish-eye routing, that are being developed in ad hoc networks, to scale techniques that are applicable to small networks to the traffic networks in large urban areas. The challenges of understanding and influencing traffic control open up new research issues in network flows, communication, optimization, and statistical modeling. All of the procedures will be tested using real data from a selected area of Manhattan. The broader impacts of this project includes: (1) It will reduce fuel consumption and commute time by reducing the time spent at traffic signals; (2) It will establish collaboration between two complementary areas with the similar goals of increasing throughput and optimizing flows in networks; (3) It will actively engage graduate and undergraduate students by developing learning modules and encouraging minority students to be involved in this interdisciplinary effort. The work will be widely disseminated to the transportation and networking communities.