Prediction of Vehicular Traffic Flow Using Optimized Neural Network

Intelligent transport systems (ITS) include a broad range of applications that require proactive strategies and predictive data driven by artificial intelligence and big data.The objective of this paper is to improve the accuracy of traffic flow prediction by utilizing a novel approach that combines feedforward neural-networks with the Quasi-Newton (QN) optimization method.The proposed method decreases the error factor based on the lagrange multiplier and Jacobian vector.This enhancement has resulted in a faster convergence during the learning process.The sample was chosen utilizing the dataset provided by the England Highway (HE) traffic monitoring systems in 2023.In order to assess the proposed model, the research findings are compared to other standard prediction techniques.As a regression model, the proposed optimized multi-layer perceptron neural network method achieves an average root-mean-squared-error of 0.143 compared to 0.319, 0.459, and 0.406 achieved by (random forest, Naïve bays, k-nearest neighbour) respectively.That is, the proposed model achieved an average percentage of improvement in prediction of are approximately 55.17%, 68.81%, and 64.78%, respectively, compared to other standard techniques.Finally, the superiority of the proposed model was evaluated by the coefficient of determination (R²) and mean-absolute-error measure, and its performance was better than other forecasting techniques as well.