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 are still not dynamically timed in response to real-time vehicle information. Congestion fees, which are increasingly popular as a traffic management 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.
This project focuses on 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. The pivotal element of the proposed Green City intelligent transport architecture is 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 and the on-board navigators play key roles. Traffic signals sense traffic characteristics and vehicular emissions, collect data from vehicle sensors, and broadcast traffic advisories, routings, and restrictions to on-board navigators. The on-board navigators choose optimal routings taking into account 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.
Broader Impact: This project is highly interdisciplinary; it benefits from the collaboration and expertise of computer science, atmospheric science, and urban planning faculty and students. New education opportunities will result from the multidisciplinary nature of the project.
