Year: 2026
Type: article
Abstract: Short-term traffic flow prediction remains challenging because traffic dynamics are nonlinear, non-stationary, and governed by both local topology and long-range directional dependencies. Existing approaches often rely on static or uniformly learned graphs, do not explicitly separate slow trends from rapid fluctuations, and insufficiently exploit correlated modalities such as flow, speed, and traffic type. To address these issues, we propose the Pivotal Multi-Modal Spatio-Temporal Network (PMST-Net), which combines a Pivotal Node Identification Module (PNIM), a Temporal Feature Disentanglement (TFD) layer, a Pivotal-Informed Spatial Attention (PISA) encoder, and a Cross-Modal Multi-Task Learning (CM-MTL) prediction head. Experiments on four public PeMS benchmarks demonstrate consistent improvements over strong baselines. On PEMS08, PMST-Net reduces mean absolute error (MAE) and root mean squared error (RMSE) by 42.3% and 37.2%, respectively, relative to the strongest baseline. Ablation, robustness, and efficiency analyses further confirm the contribution of each module and the practical value of the overall design.
Source: PeerJ Computer Science
Authors: Qiaoli Liu, Yali Zhang
Institution: SongShan ShaoLin WuShu College
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
OpenAlex ID: W7166661833