Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models

Accurate and timely flow prediction is the most significant element for intelligent traffic management systems. However, developing a robust and potential prediction method is a challenge because of the nonlinear characteristics and inherent randomness of the traffic flow in smart cities. Deep learning can analyze historical traffic data and predict future traffic patterns in traffic flow prediction. This can be done by training deep neural networks on large datasets, such as traffic speed and volume data, to learn the underlying relationships between various factors influencing traffic flow. The resulting models can then be used to predict future traffic conditions, helping optimize traffic management, reduce congestion, and improve safety. This study introduces a Hunter Prey Optimization with Hybrid Deep Learning-Driven Traffic Flow in smart cities Prediction (HPOHDL-TFPM). The HPOHDL-TFPM approach's primary goal is to accurately and rapidly forecast traffic flow. The HPOHDL-TFPM technique uses Z-score normalization to normalize the traffic data to achieve this. In addition, the CBLSTM-AE model, which combines convolutional bidirectional long short-term memory and autoencoder, is utilized in the prediction of traffic flow in smart cities. Moreover, the HPO technique is applied as a hyperparameter optimizer to select the hyperparameter values properly. The experimental validation of the HPOHDL-TFPM approach is tested in several contexts. Numerous comparative studies demonstrated the improved performance of the HPOHDL-TFPM approach over other existing methods.