Abstract
The accurate and effective prediction of the traffic flow of vehicles plays a significant role in the construction and planning of signalized road intersections. The application of artificially intelligent predictive models in the prediction of the performance of traffic flow has yielded positive results. However, much uncertainty still exists in the determination of which artificial intelligence methods effectively resolve traffic congestion issues, especially from the perspective of the traffic flow of vehicles at a four-way signalized road intersection. A hybrid algorithm, an artificial neural network trained by a particle swarm optimization model (ANN-PSO), and a heuristic Artificial Neural Network model (ANN) were compared in the prediction of the flow of traffic of vehicles using the South Africa transportation system as a case study. Two hundred and fifty-nine (259) traffic datasets were obtained from the South African road network using inductive loop detectors, video cameras, and GPS-controlled equipment. For the ANN and ANN-PSO training and testing, 219 traffic data were used for the training, and 40 were used for the testing of the ANN-PSO model, while training (160), testing (40), and validation (59) was used for the ANN. The ANN result presented a logistic sigmoid transfer function with a 13–6–1 model and a testing R2 of 0.99169 compared to the ANN-PSO result, which showed a testing performance of R2 0.99710. This result shows that the ANN-PSO model is more efficient and effective than the ANN model in the prediction of the traffic flow of vehicles at a four-way signalized road intersection. Furthermore, the ANN and ANN-PSO models are robust enough to predict traffic flow due to their better testing performance. The modelling approaches proposed in this study will assist transportation engineers and urban planners in designing a traffic control system for traffic lights at four-way signalized road intersections. Finally, the results of this research will assist transportation engineers and traffic controllers in providing traffic flow information and travel guidance for motorists and pedestrians in the optimization of their travel time decision-making.
Highlights
Thirteen input parameters and one output parameter were taken into consideration. Based on these artificially intelligent approaches (ANN and Artificial Neural Networks (ANN)-particle swarm optimization (PSO)) used for the traffic flow data, the following conclusions can be drawn from the present study: 1
One of the most significant findings to emerge from this study is that the comparison of the ANN-PSO model and ANN model has shown that the ANN-PSO model is far more accurate, easy to use, and efficient than the ANN model, with a testing performance of 0.9971, compared to the ANN model’s testing performance of 0.99169
This study suggests that a Neural Network comprising five (5) neurons is the best-performing neural network during the ANN-PSO model training of the traffic datasets
Summary
In developed and developing countries, traffic congestion at signalized road intersections has become a central issue. Efficient and effective traffic flow prediction in road transportation is one of the most fundamental characteristics of smart cities and intelligent transportation systems [1]. It is imperative to transportation researchers and pedestrians [1]. Having up-to-date traffic flow information for traffic congestions on freeways and knowing the level of the traffic volume of vehicles at road intersections in advance plays an important role in assisting transportation and civil engineers in developing and Sustainability 2021, 13, 10704.
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