**Reference:**

Z. Cong,
B. De Schutter, and
R. Babuska,
"Optimal routing in freeway networks via sequential linear
programming," *Proceedings of the 10th IEEE International
Conference on Networking, Sensing and Control*, Paris, France, 6
pp., Apr. 2013. Paper FrB01.5.

**Abstract:**

Based on the Ant Colony Optimization (ACO) algorithm, we previously
developed an optimization method to solve the dynamic traffic routing
problem in freeway networks, called Ant Colony Routing (ACR). This
method uses virtual ants to search appropriate routes in a virtual ant
network, and accordingly distributes the vehicles over the
corresponding traffic network sharing the same topology with the ant
network. By using Model Predictive Control (MPC), we can iteratively
apply ACR at each control step to generate a control signal - i.e.
splitting rates at each node in the traffic network. Motivated by the
MPC framework with ACR, we show in this paper that sequential linear
programming (SLP) can be used as optimization method for solving the
dynamic traffic routing problem in some specific cases, resulting a
lower computation time while achieving a similar performance as the
ACR algorithm.

Corresponding technical report: pdf file (208 KB)

@inproceedings{ConDeS:13-013,

author={Z. Cong and B. {D}e Schutter and R. Babu{\v{s}}ka},

title={Optimal routing in freeway networks via sequential linear programming},

booktitle={Proceedings of the 10th IEEE International Conference on Networking, Sensing and Control},

address={Paris, France},

month=apr,

year={2013},

note={Paper FrB01.5}

}

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