@article{4784,
  author = {Nguyen Minh Tuan},
  title = {A Comprehensive Multicollinearity Analysis and Ridge-Based Classification of Dynamic Routing Decisions in Internet of Vehicles (IoV) Networks},
  journal = {Journal of Networking Technology},
  year = {2026},
  volume = {17},
  number = {3},
  doi = {https://doi.org/10.6025/jnt/2026/17/3/161-180},
  url = {https://www.dline.info/jnt/fulltext/v17n3/jntv17n3_3.pdf},
  abstract = {The rapid evolution of the Internet of Vehicles (IoV) introduces significant challenges for dynamic routing
due to high mobility and complex interdependencies among network parameters. Datasets used for machine
learning based routing decisions frequently exhibit multicollinearity, leading to unstable model estimates,
inflated variances, and reduced interpretability. Traditional mitigation techniques, such as Principal
Component Analysis, often sacrifice valuable domain specific information by transforming or eliminating
variables. This study proposes a comprehensive framework combining extensive multicollinearity diagnostics
with a Ridge Classifier to enhance routing decision making in dynamic IoV networks. Using a synthetic
dataset of 4,000 instances comprising 28 numerical features, we performed rigorous diagnostics including
Variance Inflation Factor (VIF), condition indices, and Belsley analysis confirming severe feature
interdependencies. Instead of discarding variables, we applied L2 regularization via a Ridge Classifier to
stabilize coefficient estimates while preserving all domain relevant predictors. The optimized Ridge model
achieved a test accuracy of 84.63% and a 5-fold cross validation accuracy of 84.50%, significantly
outperforming a baseline Random Forest classifier at 81.13%. Additionally, implementing class weights
successfully improved minority class recall, highlighting the framework's adaptability to imbalanced routing
scenarios. Ultimately, the empirical results Proved that ridge regularization effectively mitigates numerical
instability caused by multicollinearity without sacrificing critical vehicular network metrics. This research
provides a reliable, interpretable, and computationally efficient analytical foundation for intelligent routing
protocols, contributing to more resilient next generation intelligent transportation systems. Future work
will explore real world deployment and advanced class balancing strategies.},
}