Traffic data imputation via knowledge graph-enhanced generative adversarial network

PeerJ Comput Sci. 2024 Oct 14:10:e2408. doi: 10.7717/peerj-cs.2408. eCollection 2024.

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 external factors such as points of interest (POI) and weather conditions, facilitating more robust knowledge representation learning. We then introduce a knowledge-aware embedding cell (EM-cell) that merges traffic data with these learned external representations, providing richer inputs for the spatiotemporal GAN. Extensive experiments on a large-scale real-world traffic dataset demonstrate that KG-GAN significantly outperforms state-of-the-art methods under various missing data scenarios. Additionally, ablation studies confirm the superior performance gained from incorporating external knowledge, underscoring the importance of this approach in addressing complex missing data patterns.

Keywords: Generative adversarial networks; Knowledge graph; Traffic data imputation.

Grants and funding

This work was supported by the “Pioneer” and “Leading Goose” R&D Program of Zhejiang 2023C01241, the National Natural Science Foundation of China under Grant 62072409 and Grant 62073295, and the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.