Reference
Z. Cong, B. De Schutter, and R. Babuška, "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.
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BibTeX
@inproceedings{ConDeS:13-013,
author = {Cong, Zhe and De Schutter, Bart and Babu{\v{s}}ka, Robert},
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}
}