Volume 17 , Issue 2 , PP: 82-96, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Salih Al-Obaidi 1 * , Ghaith J. Mohammed 2 , Waleed Khalid Alzubaidi 3
Doi: https://doi.org/10.54216/JCIM.170207
Vehicular Ad-hoc Networks (VANETs) play a crucial role in intelligent transportation systems, facilitating communication between vehicles and infrastructure in urban environments. Clustering algorithms are essential for managing network topology and enhancing communication efficiency in VANETs. The complex nature of three-dimensional (3D) urban environments, coupled with varying traffic conditions and driver behaviors, presents significant challenges for VANET clustering algorithms. Understanding these interactions is vital for developing robust and efficient VANETs. This study investigates how vehicle generation patterns, driving dynamics, and 3D road geometries influence the performance of VANET clustering algorithms in urban settings, focusing on network connectivity and stability. A comprehensive simulation framework was developed, incorporating a Traffic Generator model, a Mobility Model, and a Model of Road Curvature. The methodology evaluated clustering algorithm performance across three traffic congestion levels (low, medium, high) and three driver aggression levels for each congestion scenario. Data analysis, correlation studies, and sensitivity analysis were conducted to assess the impact of these factors on clustering efficiency. The study revealed significant correlations between traffic congestion levels, driver aggression, and clustering performance. Higher congestion levels led to more frequent cluster reconfigurations, while increased driver aggression affected the predictability of vehicle movements, affecting cluster stability. The 3D nature of urban environments introduced additional challenges, particularly in areas with elevation changes. The findings underscore the need for adaptive clustering algorithms capable of responding to dynamic urban traffic conditions. The research provides valuable insights for optimizing VANET clustering strategies in 3D urban environments, contributing to the development of more efficient and reliable vehicular communication networks for future smart cities.
Vehicular Ad-hoc Networks (VANETs) , Three-dimensional (3D) urban environments , Clustering algorithm performance , Mobility Model , Model of Road Curvature
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