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.
Highlights
Can a hybrid artificial intelligence algorithm such as an artificial neural network trained by particle swarm optimization (ANN-PSO) be used as a predictive approach in modeling traffic flow at a signalized road intersection?
The results of this research have proven that the artificial neural networks (ANNs)-PSO model is far more accurate, easy to use, and efficient than other predictive models when it comes to traffic flow prediction of vehicles at a signalized road intersection
This research study is designed to demonstrate how an artificial neural network trained by particle swarm optimization can model vehicles’ traffic flow at signalized road intersections using selected traffic flow inputs and output
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Intelligent transportation systems provide efficient transportation in terms of vehicles’ reduced traffic flow time on the road. It improves transportation safety and increases global connectivity in the road transportation network. A holistic approach is needed to tackle traffic flow congestion at a signalized road intersections in Gauteng province, South Africa. The novelty of this study is in the application of a hybrid artificial neural network trained by a particle swarm optimization model for modelling traffic flow at signalized road intersections. Can a hybrid artificial intelligence algorithm such as an artificial neural network trained by particle swarm optimization (ANN-PSO) be used as a predictive approach in modeling traffic flow at a signalized road intersection?. The final section comprises the conclusion and implications of this study on the road transportation system
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