  Reasonable setting of traffic signals can be very helpful in alleviating
congestion in urban traffic networks. Meta-heuristic optimization algorithms
have proved themselves to be able to find high-quality signal timing plans.
However, they generally suffer from performance deterioration when solving
large-scale traffic signal optimization problems due to the huge search space
and limited computational budget. Directing against this issue, this study
proposes a surrogate-assisted cooperative signal optimization (SCSO) method.
Different from existing methods that directly deal with the entire traffic
network, SCSO first decomposes it into a set of tractable sub-networks, and
then achieves signal setting by cooperatively optimizing these sub-networks
with a surrogate-assisted optimizer. The decomposition operation significantly
narrows the search space of the whole traffic network, and the
surrogate-assisted optimizer greatly lowers the computational burden by
reducing the number of expensive traffic simulations. By taking Newman fast
algorithm, radial basis function and a modified estimation of distribution
algorithm as decomposer, surrogate model and optimizer, respectively, this
study develops a concrete SCSO algorithm. To evaluate its effectiveness and
efficiency, a large-scale traffic network involving crossroads and T-junctions
is generated based on a real traffic network. Comparison with several existing
meta-heuristic algorithms specially designed for traffic signal optimization
demonstrates the superiority of SCSO in reducing the average delay time of
vehicles.
