## 1 Introduction
With the assistance of intelligent transportation systems, safety, effective mobility, and sustainable transportation, conducive road transportation systems can be achieved. For ITS to be achieved and implemented, an effective and efficient prediction of vehicular traffic flow is mandatory [25; 26; 27; 28; 29]. The usage of an accurate vehicular traffic-flow prediction system in ITS is to offer regular continuous and accurate road-travel information depending on the conditions of the road, such as the impacts of flow of vehicles on road intersections and freeways, which are vital to road traffic control and management and integration of road resources. The significant difficulties in applying traffic-flow prediction in intelligent transportation systems can be divided into accuracy, efficiency, and prediction [30; 31; 32; 33; 34; 35]. The foundation of efficiency issues for traffic-flow prediction is the primary characteristic of vehicular traffic-flow patterns. The patterns of vehicular traffic flow are usually rigid and not flexible. The variations in their patterns are impacted by traffic lights from the traffic flow of vehicles, unstable changes in weather, and other impeding factors. Some of these have a long-term impact, causing the traffic-flow variations to exhibit specific trends and irregularities, while there are also short-term impacts. The times series variations can be categorized into (1) _trend variation_, which can be defined as the process that allows change to occur while using time and the gradual rise, reduction, or no rise in a specific direction; (2) _periodic change_, which is also known as seasonal change and is a process that allows cyclical fluctuations at a specific period; (3) _cyclical variation_, which is defined as the processes that are unfixed and subject to periodic fluctuations; and (4) _random variation_ which is defined as when accidental constraints impact the processes and show irregular variations. The definition of time series is usually a combination of all these variations. This is due to the impact of natural and human interference on road traffic. It possessesintricate non-linear features, which causes enormous difficulties regarding the efficiency of traffic-flow prediction. The efficiency of the model used in traffic-flow prediction of vehicles depends on the implementation cost and the prediction results. The implementation cost is primarily caused by the model training and application of this model in an extensive urban road transportation system [36]. An enormous amount of time is used for predictive model training, specifically for the model associated with machine learning, such as a machine-learning-dependent model combined with the structure of deep learning. However, when applying the predictive model to a large urban road network, the distinctiveness of the characteristics of various types of road networks can be understood by using predictive models [37; 38]. These predictive models comprise different types of parameters which are associated with each segment of the road. Traffic-flow prediction has become a study hotspot in many road-transportation systems, especially in road intersections, freeway congestions, and un-signalized road intersections. Even though ANFIS combines the learning power of artificial neural networks with fuzzy logic knowledge representation, some complexities exist when creating membership functions in ANFIS. The foundation of these complexities can be found when tuning the ANFIS function to create the optimal model with high efficiency and optimal performance features. The primary aim of combining genetic algorithm (GA) with ANFIS is to decrease errors by tuning and optimizing processes on the ANFIS membership functions.