## 2 Literature Review  
### Related Studies  
In recent years, there has been a growing body of literature review on particle swarm optimization (PSO) and its application in the field of engineering. Prior studies have stated that the application of particle swarm optimization in engineering applications is broad. However, its application to the best of authors' knowledge in the area of road intersectionsis limited. The importance of particle swarm optimization cannot be underemphasized due to its hybrid properties and its superior artificial intelligence. [PERSON] et al. propose various applications of particle swarm optimization for the scheduling of railway operations and planning of the railway network layout [12]. They used PSO to find the best optimal railway schedules. [PERSON] et al. [12] Implemented PSO in the effective electric power system. [PERSON] et al. [13] used different forms of objectives to solve a multi-objective load problem by applying PSO. Furthermore, additional related studies on particle swarm optimization are its application in determining the optimal performance of power flow [14; 15], load flow [16; 17], and the design of a proportional integral derivative (PID) controller in a system that uses an automatic voltage regulator (AVR) [18] and finally in the decreasing of power loss [19]. In civil engineering, especially in construction, [20] came up with a PSO algorithm to design a rectangular flow in the form of a beam slab. [PERSON] et al. [21] suggested an efficient hierarchical global path planning technique for non-stagnant robots in a chaotic engineering environment by applying particle swarm optimization. [PERSON] et al. [22] proposed a function called time-varying transfer, which can be found in binary particle swarm optimization (BPSO). This BPSO is called time-varying transfer function-based binary PSO (TVT-BPSO). The aim was to use this BPSO to evaluate its exploration and exploitation abilities critically. [PERSON] et al. [23] used PSO in the medical field to predict the duration of stay of sick patients in the hospital, and the key finding of this research was that PSO was efficient compared to other predictive models. Some other applications of PSO over the years have expanded to other engineering applications such as source seeking problem [24], elevator door conceptual framework [25], the problem of quad assignment [26], determining the quantity of equipment in possession [27], and the problem of job shop scheduling [28]. Table 1 below shows previous research been done by researchers on different types of particle swarm optimization (PSO). There are also different types of PSO that various researchers have used; they are:  
* Multi-objective optimization by implementing PSO. Researchers have used this approach in solving various optimization problems by using a combination of PSO and discrete multi-objective PSO [32]. In addition to multi-objective methods such as competitive cooperative and co-evolutionary approaches [33] and a PSO evaluation by a vector [34]. * Modified Particle swarm optimization. There have been many modifications to PSO to improve its optimization performance and applicability. Notable among these modified PSOs are memory enhanced PSO [35], predator-prey PSO [36]. However, there are newer modified PSOs such as comprehensive learning particle swarm optimizer (CLPSO) [37], self-learning particle swarm optimization (SLPSO) [38], and orthogonal learning particle swarm optimization (OLPSO) [39]. The unique thing about this newer variant of PSO is that they do not need tuning of