A Neural Network Approach to GSM Traffic Congestion Prediction

Provided by: American journal of Engineering Research (AJER)
Topic: Mobility
Format: PDF
In this paper, the authors propose a GSM congestion prediction model based on Multi-Layer Perceptron Neural Networks (MLP-NNs) with sigmoid activation function and Levenberg-Marquardt Algorithms (LMA) using twelve month real traffic data. The trained network model was used to predict traffic congestion along a chosen route. Regression analysis between predicted traffic congestion volumes and corresponding actual traffic congestion volumes shows a correlation coefficient of 0.986. This result clearly shows the effectiveness of Artificial Neural Networks (ANN) in traffic congestion prediction and control.

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