Traffic data imputation <i>via</i> knowledge graph-enhanced generative adversarial network.

ABSTRACT: Traffic data imputation is crucial for the reliability and efficiency of intelligent transportation systems (ITSs), forming the foundation for downstream tasks like traffic prediction and management. However, existing deep learning-based imputation methods struggle with two significant challenges: poor performance under high missing data rates and the limited incorporation of external traffic-related factors. To address these challenges, we propose a novel knowledge graph-enhanced generative adversarial network (KG-GAN) for traffic data imputation. Our approach uniquely integrates external knowledge with traffic spatiotemporal dependencies to improve data imputation quality. Specifically, we construct a fine-grained knowledge graph (KG) that differentiates attributes and relationships of e

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11623069 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature