TY  - EJOU
AU  - Alanazi, Meshari D. 
AU  - Elsayed, Gehan 
AU  - Alanazi, Turki M. 
AU  - Sahbani, Anis 
AU  - Yousef, Amr 

TI  - Graph Neural Network-Assisted Lion Swarm Optimization for Traffic Congestion Prediction in Intelligent Urban Mobility Systems
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 2
SN  - 1526-1506

AB  - Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality. Then, the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment. From the connectivity and node relationship graph, modeling improves the overall prediction accuracy. During the analysis, the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies, which helps predict high congestion on roads with minimal deviation errors. There are three core optimization phases: roaming, hunting, and migration, which enable the framework to make dynamic adjustments to enhance the predictions. The framework’s efficacy is evaluated using benchmark datasets, where the proposed work achieves 99.2% accuracy and minimizes the prediction deviation value by up to 2.5% compared to other methods. With the new framework, there was a more accurate prediction of real-time congestion, lower computational cost, and improved regulation of traffic flow. This system is easily implemented in intelligent transportation systems, smart cities, and self-driving cars, providing a robust and scalable solution for future traffic management.
KW  - Intelligent transportation systems; traffic congestion; graph neural networks; lion swarm optimization; traffic dependencies; smart cities

DO  - 10.32604/cmes.2025.070726