Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities

The rapid urbanization accompanying the evolution into "smart" communities presents numerous challenges, not least of which is the significant increase in road vehicles.This proliferation exacerbates congestion and accident rates, posing major barriers to the successful implementation of innovative technologies such as Wireless Sensor networks (WSNs), surveillance cameras, and the Internet of Things (IoT).Accurate traffic flow prediction, a crucial component of these technological initiatives, requires a reliable and efficient methodology.This research explores the implementation of an intelligent traffic control system that employs a Transferable Texture Convolutional Neural Network (TTCNN).The design of this system eschews the traditional pooling layer, instead incorporating three convolutional layers and a single Energy Layer (EL).This configuration facilitates the provision of real-time traffic updates, which can enhance the utility and efficiency of the smart city infrastructure.A model inspired by the Hybrid Fruit Fly (HFFO) optimizes the system's hyperparameters.The application of HFFO to the TTCNN showcases the potential for improved accuracy in traffic flow prediction.Simulation results suggest that the HFFO provides superior organizational boundaries for the TTCNN, enhancing the overall accuracy of the model's predictions.The hybrid forecasting method discussed herein demonstrates its potential to outperform other established techniques.This investigation sheds light on the potential benefits of applying deep learning algorithms and hybrid models in the context of traffic flow prediction and control, contributing to the ongoing development of smart urban communities.