An Artificial Neural Network Model for Short-Term Traffic Flow Prediction in Two Lane Highway in Khulna Metropolitan City, Bangladesh
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Abstract
Short-term traffic flow prediction is one of the most significant research topics in traffic engineering. It is instrumental in designing a more modern transport network to manage traffic signals and reduce congestion. Short-term traffic flow is a challenge that a third-world country like Bangladesh is all too familiar with. Like the other cities of Bangladesh, Khulna Metropolitan City is gradually becoming more aware of this situation. The Khulna-Jashore National Highway (N-7), which runs through the city and provides a linear shape, serves as the backbone of the Khulna Metropolitan City traffic flow. This study developed an Artificial Neural Network (ANN) model for the short-term Traffic Flow Prediction on Two-Lane Highway in Khulna Metropolitan City, Bangladesh. Data was collected from March 1, 2021, through June 30, 2021, during 600–900 hours and 1200–1500 hours. Good-quality electronic cameras recorded the vehicles at the full designated length. The regression graphs displayed the network outputs with targets for the training, validation, and test sets. The various speed level parameters for which the fit is reasonable for all data sets, with R values of 0.98426 in each case. The various traffic volume parameters for which the fit is reasonable for all data sets, with R values of 0.96758 in each case. The model's superiority is indicated by its low mean squared error values. This study demonstrated that the neural network has a good prediction effect on specific road traffic flow, which can achieve the goal of short-term prediction and has improved practicability through testing on real traffic data. This study provides an opportunity to provide a suitable alternative for short-term traffic flow forecasting in Khulna Metropolitan City with traffic flow conditions for two-lane undivided highways.
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