Computational Thinking (CT) brought revolutionary productivity increases in many service sectors, but it has not touched the road transportation service sector whose productivity has been declining for a long time. The proposed research will enable a major increase in road transportation productivity through a dramatic improvement in all major components of traffic operations: data collection and processing; model calibration and state estimation; and planning, design and implementation of real-time control. The CDI themes figure in the three major research tasks of the proposed effort. Understanding complexity A road network is a large interconnected system of linkssegments of freeways and urban streetson which vehicles and drivers move and interact to create a complex, nonlinear, socio-physical dynamical traffic system. Link junctions have programmable control devices (intersection signals, ramp meters, message signs announcing speed limits, tolls, traveler information and advice, etc.) that regulate the evolution of traffic. A major increase in efficiency of the road network can be achieved by developing and implementing sophisticated feedback policies for these control devices. The research needed to develop these policies requires understanding the behavior of the underlying dynamical traffic system, based on mathematical analysis and simulation. This is the first major task. Data to models The specification and calibration of nonlinear dynamical models of the traffic system require large amounts of historical traffic data. The implementation of feedback algorithms requires estimates of the current state of the traffic system, based on real-time measurements. The research will develop procedures for statistical estimation of model parameters, visualization of archived data and simulations, and model-based processing of real-time traffic sensor data. This is the second major task. Virtual organization Transportation authorities must implement the feedback control policies, but they lack necessary skills and knowledge of computational thinking. Thus collaboration between academic researchers and transportation personnel is needed to train the latter in computational thinking and to educate the former about the institutional and other real-world constraints facing transportation personnel. Such collaboration requires trust underlying a true partnership, and assistance with electronic means to facilitate cooperation among physically and institutionally remote groups. Building this collaboration and implementing the proposed feedback algorithms is the third major task.
