Applying Neural Networks on Traffic Forecasting

Urban traffic congestion poses a substantial risk to economic productivity and efficiency. Traditional infrastructure solutions are no longer able to meet the demand for traffic flow management as metropolitan populations continue to grow. Three models—Long Short-Term Memory (LSTM), Convolutional Neural Network and Long Short-Term Memory Hybrid Model (CNN-LSTM), and Diffusion Convolutional Recurrent Neural Network (DCRNN)—are used in this study to investigate the use of neural networks in the traffic forecasting problem. Key elements of traffic data, particularly temporal and spatial dependencies, are captured by each model. Our tests show that the hybrid and DCRNN models successfully combine temporal and spatial dynamics, whereas the LSTM model has trouble capturing spatial linkages. The models showed better-predicting abilities as the neural network grew more sophisticated and skilled at the tasks. Notably, the integration of Convolutional Neural Networks (CNNs) has resulted in a 19% reduction in the Mean Absolute Error (MAE) loss of Long Short-Term Memory (LSTM) models. Moreover, Diffusion Convolutional Recurrent Neural Networks (DCRNNs) achieved impressive accuracy, with an MAE loss that is only 57% of that observed in LSTM models. The findings stress the significance of employing suitable neural networks for the traffic forecasting task. Future researchers should consider incorporating contextual data as well as expanding datasets in order to enhance prediction accuracy with wider model capacity.