**Reference:**

S. Lin,
B. De Schutter,
Y. Xi, and
H. Hellendoorn,
"Model predictive control for urban traffic networks via MILP,"
*Proceedings of the 2010 American Control Conference*,
Baltimore, Maryland, pp. 2272-2277, June-July 2010.

**Abstract:**

Model Predictive Control (MPC) is an advanced control strategy that
can easily coordinate urban traffic networks. But, due to the
nonlinearity of the traffic model, the optimization problem of the MPC
controller will become intractable in practice when the scale of the
controlled traffic network grows larger. To solve this problem, the
nonlinear traffic model is reformulated into a model with only linear
equations and inequalities. Mixed-Integer Linear Programming (MILP)
algorithms can efficiently solve the reformulated optimization
problem, and guarantee the global optimum at the same time. Moreover,
the MILP optimization problem is further relaxed by model reduction
and adding upper bound constraints.

Corresponding technical report: pdf file (123 KB)

@inproceedings{LinDeS:10-007,

author={S. Lin and B. {D}e Schutter and Y. Xi and H. Hellendoorn},

title={Model predictive control for urban traffic networks via {MILP}},

booktitle={Proceedings of the 2010 American Control Conference},

address={Baltimore, Maryland},

pages={2272--2277},

month=jun # {--} # jul,

year={2010}

}

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