Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities
Journal Title: Mechatronics and Intelligent Transportation Systems - Year 2023, Vol 2, Issue 2
Abstract
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.
Authors and Affiliations
Ghamya Kotapati, Mohd Anwar Ali, Ramesh Vatambeti
Keywords
Related Articles
- EP ID EP732238
- DOI https://doi.org/10.56578/mits020204
- Views 64
- Downloads 0
Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model
Road maintenance is essential to the growth of the transportation infrastructure and, thereby, has a big impact on a nation's overall economic stability and prosperity. It is impossible to simultaneously monitor and main...
Enhancing Urban Traffic Management through YOLOv5 and DeepSORT Algorithms within Digital Twin Frameworks
The acceleration of urbanization and the consequent increase in population have exacerbated urban road traffic issues, such as congestion, frequent accidents, and vehicle violations, posing significant challenges to urba...
Design and Testing of Cooperative Motion Controller for UAV-UGV System
Unmanned ground vehicles (UGVs) and quadrotor unmanned aerial vehicles (UAVs) can work together to solve challenges like intelligent transportation, thanks to their excellent performance complements in perception, loadin...
A Robust Road Image Defogging Framework Integrating Pythagorean Fuzzy Aggregation, Gaussian Mixture Models, and Level-Set Segmentation
Foggy road conditions present substantial challenges to road monitoring and autonomous driving systems, as existing defogging techniques often fail to accurately recover structural details, manage dense fog, and mitigate...
Optimized Collaborative Scheduling of Unmanned Aerial Vehicles for Emergency Material Distribution in Flood Disaster Management
The effective allocation of emergency supplies is crucial in the aftermath of flood disasters, as it directly impacts response times and mitigates casualties and property losses. Traditional methods of material distribut...