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

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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.