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Reasonable setting of traffic signals can be very helpful in alleviatingcongestion in urban traffic networks. Meta-heuristic optimization algorithmshave proved themselves to be able to find high-quality signal timing plans.However, they generally suffer from performance deterioration when solvinglarge-scale traffic signal optimization problems due to the huge search spaceand limited computational budget. Directing against this issue, this studyproposes a surrogate-assisted cooperative signal optimization (SCSO) method.Different from existing methods that directly deal with the entire trafficnetwork, SCSO first decomposes it into a set of tractable sub-networks, andthen achieves signal setting by cooperatively optimizing these sub-networkswith a surrogate-assisted optimizer. The decomposition operation significantlynarrows the search space of the whole traffic network, and thesurrogate-assisted optimizer greatly lowers the computational burden byreducing the number of expensive traffic simulations. By taking Newman fastalgorithm, radial basis function and a modified estimation of distributionalgorithm as decomposer, surrogate model and optimizer, respectively, thisstudy develops a concrete SCSO algorithm. To evaluate its effectiveness andefficiency, a large-scale traffic network involving crossroads and T-junctionsis generated based on a real traffic network. Comparison with several existingmeta-heuristic algorithms specially designed for traffic signal optimizationdemonstrates the superiority of SCSO in reducing the average delay time ofvehicles.

Surrogate-assisted cooperative signal optimization for large-scale traffic networks