## 5 Conclusions and Future Work  
To achieve the aim of this research study, an in-depth and exhaustive research study has been conducted to develop an ANN-PSO capable of modelling and analysing vehicular traffic flow at a signalized road intersection. For the accurate modelling of traffic flow, it was important to understand and identify some traffic flow parameters and have clarity on how traffic flow affects the movement of vehicles at a signalized road intersection. In this research study, the successful development of the ANN-PSO model for the traffic flow of vehicles at a signalized road intersection has been achieved. The vehicular speed, the time of the day, the traffic volume, and the traffic density of each class of vehicles (light vehicles, long trucks, medium trucks, and short trucks) have been considered as the traffic flow inputs and outputs variables. The following conclusions can be drawn from this present study:  
* ANN-PSO model is potentially suitable for the prediction and analysis of traffic flow at a signalized road intersection. This model could be used to predict traffic flow with a high level of accuracy. It explains the heterogeneous traffic flow conditions at different periods of the day. * Due to the stochastic nature of traffic information, it is difficult to determine the volume of traffic flow at a signalized road intersection. This equally implies that the specific time of the day determines the traffic density and vehicular speed on the road. The evidence from this study suggests that traffic density and traffic volume are significant in determining traffic congestion and understanding the traffic flow patterns on a road transportation network. * The ANN-PSO model developed in this study will assist transportation engineers and urban planners in developing possible ways to use their respective country's traffic information to understudy traffic flow patterns and variables for effective predictive models. Also, designing a traffic control system for traffic lights at road intersections can be made possible and timely. * The results of this study will serve as a base for future studies for engineers and transportation researchers in understanding the complexity of traffic flow patterns at a signalized road intersection. Also, it will assist drivers in the decision-making process, such as which period of the day traffic congestion is likely to occur on a particular road. Based on the results obtained from this research study, this research study has thrown up many questions in need of further investigation in the field of transportation:
* Further work needs to be done to establish whether other metaheuristic algorithms, such as the second generation of particle swarm optimization (SGPSO), bee colony, an artificial neural network trained by genetic algorithm (ANN-GA), adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) and simulated annealing can be used in developing predictive models using traffic flow parameters obtained from a signalized road intersection. * A natural progression of this research study would be to focus on unsignalized road intersections, traffic light timing response optimization, and the usability of traffic volume in determining traffic congestions at road intersections. Besides, demonstrating other metaheuristic techniques' strength and predictive power will be very useful as a comparative measure for minimizing traffic issues in road transportation. * Finally, another possible area of future research would be to investigate if the optimal solution obtained in this research depends on factors affecting traffic flow and how could the optimal solution change depending on these factors. Supervision, L.K.T. and M.O.O. ; Writing--original draft, I.O.O. ; Writing--review and editing, L.K.T., M.O.O. and U.F.U. All authors have read and agreed to the published version of the manuscript. The research and the APC were funded by a research grant from the University of Johannesburg. **Institutional Review Board Statement**: Not applicable.