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

利用实时网络技术优化城市交通信号可以显著提高交通系统的性能。该项目的目的是应用通信技术和通信网络技术来控制交通信号和车辆。这是一项跨学科的建议，将基于交通工程中使用的测量数据不确定性的优化技术与基于通信网络中使用的实时测量和数据传播的分布式控制策略相结合。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，然后进一步优化主干道上的流量，最后优化整个交通网络的流量。首先，我们将基于实时流量信息控制交通信号，然后发展到使用信号控制车辆路径，使用偏转路由技术，最后对单个车辆进行路线规划。我们将使用聚类技术和信息简化技术，例如在自组织网络中开发的鱼眼路由，将适用于小型网络的技术扩展到大城市地区的交通网络。理解和影响交通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。所有的程序都将使用曼哈顿选定地区的真实数据进行测试。该项目的更广泛影响包括：(1)减少在交通信号处花费的时间，从而减少燃油消耗和通勤时间；(2)它将在两个互为补充的领域之间建立合作，这些领域具有增加吞吐量和优化网络流量的类似目标；(3)通过开发学习模块和鼓励少数民族学生参与这一跨学科努力，积极吸引研究生和本科生。这项工作将广泛散发给运输和网络界。

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.