###### Abstract  
The tremendous increase in vehicular navigation often witnessed daily has elicited constant and continuous traffic congestion at signalized road intersections. This study focuses on applying an artificial neural network trained by particle swarm optimization (ANN-PSO) to unravel the problem of traffic congestion. Traffic flow variables, such as the speed of vehicles on the road, number of different categories of vehicles, traffic density, time, and traffic volumes, were considered input and output variables for modelling traffic flow of non-autonomous vehicles at a signalized road intersection. Four hundred and thirty-four (434) traffic datasets, divided into thirteen (13) inputs and one (1) output, were obtained from seven roadsites connecting to the N1 Allandale interchange identified as the busiest road in Southern Africa. The results obtained from this research have shown a training and testing performance of 0.98356 and 0.98220. These results are indications of a significant positive correlation between the inputs and output variables. Optimal performance of the ANN-PSO model was achieved by tuning the number of neurons, accelerating factors, and swarm population sizes concurrently. The evidence from this research study suggests that the ANN-PSO model is an appropriate predictive model for the swift optimization of vehicular traffic flow at signalized road intersections. This research extends our knowledge of traffic flow modelling at a signalized road intersection using metaheuristics algorithms. The ANN-PSO model developed in this research will assist traffic engineers in designing traffic lights and creation of traffic rules at signalized road intersections. traffic congestion; artificial neural network-particle swarm optimization; signalized road intersections; traffic flow modelling; traffic flow +
Footnote †: journal: Applied Sciences  
has become a widely acknowledged issue challenging to solve. This societal problem has spread majorly in the urban area of the world. The primary objectives of transportation researchers and Government administrators nowadays are to eliminate traffic congestion and assist urban planners with solutions to traffic congestion problems. Although efforts have been made and incentives and strategies have been implemented to reduce traffic congestion in megacities, new traffic congestion problems kept re-occurring and with a high level of unpredictability, especially in developing countries even though pre-existing traffic problems have been ameliorated. Sometimes, it is difficult to comprehend if the traffic congestion measures that have been put in place by urban planners and transportation engineers will either work or not. Intelligent transportation systems provide efficient transportation in terms of vehicles' reduced traffic flow time on the road. It also improves transportation safety and increases global connectivity in the road transportation network. As a result of the energy crisis the world is currently experiencing because of severe carbon monoxide from vehicles' exhaust pipes, the need for artificial intelligence techniques cannot be underemphasized. The revolution of the transportation sector and the fast-rising interest in intelligent transportation systems are due to the persistent traffic congestion on highways and freeways and the synergy of new information technology needed for real-time traffic simulation. Reinvigorating road transportation systems is crucial for implementing the fourth industrial revolution in road transportation networks. This research study used the South African Road transportation network as a case study. A holistic approach is needed to tackle traffic flow congestion at a signalized road intersections in Gauteng province, South Africa. Gauteng is vital to the South African economy; therefore, there is a need for an advanced and well-managed transportation system [9]. The population of urban cities in South Africa has increased rapidly over the last ten years [10].