## 4 Conclusions and Recommendations  
The main goal of the current study was to determine the prediction and modelling of data-driven novel vehicular traffic flow of non-autonomous vehicles at a signalized road intersection using optimized fuzzy inference systems with genetic algorithms. Based on the results and discussions, the following conclusions can be drawn:  
* The relevance of traffic volume in evaluating the vehicular traffic flow at road intersections was supported by the ANFIS and ANFIS-GA models' research findings in predicting and modelling traffic volume of vehicles. * The obvious finding from this study is that the ANFIS-GA (\(R_{Training}=0.8979\) and \(R_{Testing}=0.9980\)) results when compared to the ANFIS (\(R_{Training}=0.9709\) and \(R_{Testing}=0.9790\)) is more suitable and appropriate for the modelling and predicting of traffic volume. * The second significant finding was that this research study has torchit the importance of off-peak and on-peak periods in evaluating the vehicular traffic flow at signalized road intersections. * The evidence from the ANFIS and ANFIS-GA model results suggests that the traffic data inputs, and outputs (traffic volume, time, and speed of vehicles) are well correlated to each other. * The adaptive neuro-fuzzy inference system (ANFIS) and adaptive neuro-fuzzy inference system optimized by genetic algorithm (ANFIS-GA) are reliable traffic-flow predictors of traffic volume at road intersections. * This research has shown that the important advantage of using a hybrid ANFIS-GA in modelling the traffic flow of vehicles is that it tunes the ANFIS model's membership functions to reduce the error parameters during the training and testing of the models. The findings of this study make a significant contribution to the current literature on traffic-flow prediction and how important it is to traffic-volume prediction in reducing traffic congestion at signalized road intersections. Recommendations
* Further research needs to be carried out to improve the efficiency of traffic-volume prediction. A spatial relationship between light-, heavy-, and truck-traffic volume should be investigated. * Further study can be carried out to evaluate the effects of other traffic-flow parameters (traffic density and speed of vehicles) on the modelling of traffic volume. * Further study could assess the effects of genetic and particle swarm optimization algorithms on vehicle traffic volume at unsignalized road intersections. * Finally, further study should focus on the comparison of the results of this research with other machine-learning models. * A major limitation of this study is that it does not consider the different weather that can influence the collection of traffic datasets and flow of vehicles. Future transportation research should focus on factoring in the interference of different seasons and weather conditions on the flow of vehicles and how this can impede the collection of traffic datasets on freeways and road intersections. Conceptualization, I.O.O. and L.K.T. ; methodology, I.O.O., L.K.T. and F.J.A. ; validation, I.O.O., L.K.T. and F.J.A. ; formal analysis, I.O.O. and F.J.A. ; investigation, I.O.O. and L.K.T. ; writing--original draft preparation I.O.O. and F.J.A. ; writing--review and editing, I.O.O. and F.J.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not Applicable. Data Availability Statement: The traffic data used for this research are available on reasonable request from the corresponding author. The ANFIS-GA codes used in this research can be found in [[https://yarpiz.com/319/ypfz104-evolutionary-anfis-training](https://yarpiz.com/319/ypfz104-evolutionary-anfis-training)]([https://yarpiz.com/319/ypfz104-evolutionary-anfis-training](https://yarpiz.com/319/ypfz104-evolutionary-anfis-training)) (accessed on 15 July 2022). The authors acknowledge the University of Johannesburg, South Africa; the University of Wollongong, NSW, Australia; and the Wuhan University of Technology, China.