Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.knosys.2021.107542 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Traffic signal optimization,Large-scale traffic network,Cooperative co-evolution,Surrogate model,Estimation of distribution algorithm | Journal | 234 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongsheng Liang | 1 | 84 | 12.98 |
| Ren Zhigang | 2 | 18 | 3.69 |
| Lin Wang | 3 | 5 | 2.16 |
| Hanqing Liu | 4 | 0 | 0.34 |
| Wenhao Du | 5 | 0 | 0.34 |