Traffic congestion has become one of the major problems of smart travel. The application of evolutionary computation (EC) for traffic signal timing optimization (TSTO) can effectively alleviate traffic congestion at a single intersection. However, while in more complicated regional traffic signal timing optimization (RTSTO) problems, the canonical EC algorithm such as particle swarm optimization (PSO) still has limitation due to population prematurity. In this paper, a multi-exemplar learning (MEL) strategy is adopted to improve the diversity of the population, so that the particles can have more opportunities to explore the search space. Furthermore, multiple traffic indicators are used in this paper to measure the comprehensive performance of the solution. Moreover, the microsimulation software is adopted to evaluate the solution to simulate the real-world intersection, making the obtained solution more practical in real-world application. Experiments are conducted to investigate the effectiveness and efficiency of MEL-PSO. The results show that the MEL-PSO algorithm is more effective and efficient than the compared algorithms on RTSTO problems.
|Title of host publication||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|Publication status||Published - Oct 2021|
|Event||2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Melbourne, Australia|
Duration: 17 Oct 2021 → 20 Oct 2021
|Conference||2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)|
|Period||17/10/21 → 20/10/21|
Bibliographical noteSupported by the National Natural Science Foundations of China (NSFC) under Grants 62176094.
- multi-exemplar learning
- particle swarm optimization
- Regional traffic signal timing optimization