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Traffic control for large-scale urban traffic networks via MPC

Project members:  S. Lin, prof. B. De Schutter, hhellendoorn
Keywords:  Model predictive control, Transportation and infrastructure, Distributed and large-scale systems, Old - project will be removed
Sponsored by:  Delft University of Technology
As an advanced control methodology, Model Predictive Control (MPC) offers a lot of advantages for controlling urban traffic networks. MPC predicts the future traffic states based on the prediction model, so as to make long-term control decisions. MPC is robust to the uncertainty of the process, which can be caused by the unpredictable disturbances, the (slow) variation over time of the parameters, and model mismatches in the prediction model. MPC can easily coordinate multiple intersections and also multiple control measures. Another advantage of MPC is that one can easily select and replace the prediction model based on the control requirements. However, one problem that needs to be overcome when implementing the MPC algorithm in a real-life traffic environment is the on-line computational complexity.

Therefore, our research focuses on designing MPC controllers that are effective and also efficient for application in large-scale urban traffic networks. In order to build more efficient MPC controllers for urban traffic networks, the following approaches are considered: First, reducing the urban traffic model, which is taken as the prediction model, to achieve more efficiency for the MPC controller. Second, approximating the optimization problem by one that can be solved more efficiently. Third, dividing the network into small sub-networks, and building distributed controllers. Moreover, as performance objectives for the MPC controllers we consider the reduction of the travel times, the reduction of the traffic emissions, or a combination of both.

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Last modified: 5 July 2013, 15:30 UTC
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