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

TL;DR

This paper introduces a surrogate-assisted cooperative optimization method that decomposes large traffic networks into manageable sub-networks, significantly improving traffic signal timing and reducing vehicle delays.

Contribution

It proposes a novel SCSO approach that decomposes large networks and uses surrogate models to efficiently optimize traffic signals, outperforming existing algorithms.

Findings

SCSO reduces average vehicle delay more effectively.

Decomposition narrows the search space significantly.

Surrogate models cut computational costs.

Abstract

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.