Abstract
With the development of intelligent transportation systems, research into intelligent traffic signal control has received considerable attention. To date, many traffic signal control models have been studied, where most of the models concentrate on how to minimize travel time, vehicle delay, and the number of stops or how to maximize capacity. This study introduces the Garra Rufa-inspired (GRI) algorithm, which is used to optimize traffic signal control modelling considering the number of vehicles in a queue. GRI has the characteristics of using the decision variables of the code as the operation object, directly using the objective function value for the search information, using multiple search points at the same time, and using probability search technology. Theoretical analysis of intelligent optimization and research into application methods were carried out to resolve the problem of traffic signal optimization control. The output of the GRI algorithm was compared, calibrated, and validated with SIDRA. Furthermore, to obtain more comprehensive results, the genetic algorithm (GA) and particle swarm optimization (PSO) were also compared. The results of the analysis show that the GRI decreases by 10.1% (intersection A) and 16.5% (intersection B) in the number of vehicles in the queue.