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.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Darleen L. Fisher
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University of California Los Angeles
Los Angeles
United States
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