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

NetSE：小型：协作研究：将实时交通信号控制与网络控制策略相结合以优化城市交通网络

• 批准号: 0915026
• 负责人: Nicholas Maxemchuk
• 金额: $ 25万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915026&HistoricalAwards=false
• 关键词: NetSE; Small; Collaborative; Research; Integrating

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.

利用实时网络技术来优化城市交通信号，可以显着提高运输系统的性能。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。这是一个跨学科的建议，它将基于在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中基于不确定性以及分布式控制策略的测量数据中的不确定性结合优化技术。研究工作被组织为有序的进展，该进展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。我们将首先优化孤立的交通信号处的流量，然后发展到动脉上的流，最后到整个交通网络中的流动。最初，我们将根据实时流量信息控制交通信号，然后进步使用信号来控制车辆路径，使用偏转路由技术，最后对单个车辆进行路线计划。我们将使用在临时网络中开发的聚类技术和减少信息的技术（例如鱼眼路由）来扩展适用于小型城市地区交通网络的小型网络的技术。理解和影响交通控制的挑战开辟了网络流，通信，优化和统计建模的新研究问题。 所有程序都将使用曼哈顿选定区域的实际数据进行测试。该项目的更广泛影响包括：（1）通过减少交通信号所花费的时间来减少燃油消耗和通勤时间； （2）它将在两个互补领域之间建立合作，其目标的类似目标是增加网络中的吞吐量和优化流量； （3）它将通过开发学习模块并鼓励少数群体参与这一跨学科工作来积极参与研究生和本科生。这项工作将被广泛传播到运输和网络社区。