B. De Schutter, H. Hellendoorn, A. Hegyi, M. van den Berg, and S.K. Zegeye, "Model-based control of intelligent traffic networks," Chapter 11 in Intelligent Infrastructures (R.R. Negenborn, Z. Lukszo, and H. Hellendoorn, eds.), vol. 42 of Intelligent Systems, Control and Automation: Science and Engineering, Dordrecht, The Netherlands: Springer, ISBN 978-90-481-3598-1, pp. 277-310, 2010.
Road traffic networks are increasingly being equipped and enhanced with various sensing, communication, and control units, resulting in an increased intelligence in the network and offering additional handles for control. In this chapter we discuss some advanced model-based control methods for intelligent traffic networks. In particular, we consider model predictive control (MPC) of integrated freeway and urban traffic networks. We present the basic principles of MPC for traffic control including prediction models, control objectives, and constraints. The proposed MPC control approach is modular, allowing the easy substitution of prediction models and the addition of extra control measures or the extension of the network. Moreover, it can be used to obtain a balanced trade-off between various objectives such as throughput, emissions, noise, fuel consumption, etc. Moreover, MPC also allows the integration and network-wide coordination of various traffic control measures such as traffic signals, speed limits, ramp metering, lane closures, etc. We illustrate the MPC approach for traffic control with two case studies. The first case study involves control of a freeway stretch with a balanced trade-off between total time spent, fuel consumption, and emissions as control objective. The second case study has a more complex layout and involves control of a mixed urban-freeway network with total time spent as control objective.