Key Node Identification in Transportation Networks Based on the K-Shell Algorithm: Applications in Traffic Flow Prediction
TL;DR
The results highlight the robustness and versatility of the proposed framework in real-world traffic scenarios, demonstrating its potential to enhance prediction accuracy and scalability through key-node identification and selective coverage analysis.
Abstract
This study proposes an enhanced key-node-driven framework for traffic flow prediction in large-scale transportation networks. Building upon the classical K-shell decomposition, the proposed method integrates traffic-flow-based weighting to jointly capture structural hierarchy, functional relevance, and global topological influence of network nodes. By ranking node importance through composite indicators lambda-c, lambda-f, and lambda-s, the framework identifies structurally dominant nodes and evaluates their effectiveness across multiple traffic prediction models. Comprehensive experiments conducted on three benchmark datasets—PEMS04, METR-LA, and PEMS-BAY—demonstrate that graph-based spatiotemporal models such as ST-GCN, GraphWaveNet, DCRNN, STDN, STTN, and SWAVE maintain high predictive accuracy even under reduced node coverage. In particular, DCRNN exhibits strong robustness in capturing dynamic spatiotemporal dependencies, while STTN effectively models long-term temporal patterns. Overall, the results highlight the robustness and versatility of the proposed framework in real-world traffic scenarios, demonstrating its potential to enhance prediction accuracy and scalability through key-node identification and selective coverage analysis.