Optimizing Urban Traffic Incident Prediction With Vertical Federated Learning: A Feature Selection Based Approach

Federated learning constitutes a collaborative and shared machine learning paradigm facilitating the joint development of a global model, distinctively addressing privacy concerns while integrating data from various sources. Urban traffic incident prediction (UTIP) tasks inherently require cross-departmental data collaboration, underscoring the significance of federated learning. Specifically, vertical federated learning (VFL) enables multiple participants, each possessing non-overlapping feature subsets, to collectively train predictive models. Recently, researchers have focused on specific VFL issues, such as feature selection and privacy. This study provides a methodology for developing a VFL model utilizing a significant feature selection strategy. The proposed framework is called feature selection-based VFL traffic incident prediction (FSVFL-TIP), and specifically intends to improve incident prediction accuracy. The effectiveness of our suggested model is studied and compared to the baseline VFL model, revealing that our approach outperforms the baseline by 5.7% to 11.6% in test accuracy on two publicly available traffic datasets. Finally, this study explores the improvement in accuracy under various VFL split configurations. The results indicate that VFL is a preferable solution for improved accuracy and communication efficiency while using high-performing feature selection strategies.