Multi-step short-term traffic speed prediction by fusing external attributes and spatiotemporal features
LI Zhihong
ZHAO Xia
SHI Zhuoya
TANG Jiali
YUAN Zhenzhou
ZHANG Yi
GAO Yuan
Abstract：Highway traffic speed is jointly influenced by multiple external factors,including adjacent land-use types,weather conditions,and traffic volume,and exhibits nonlinear spatiotemporal varia-tions.To address this,this paper proposes WP-STGCN-GRU,a multi-step short-term traffic speed prediction model that integrates external attributes with spatiotemporal features.First,a spatiotempo-ral prediction framework is constructed using the Spatial-Temporal Graph Convolutional Network(STGCN)and Gated Recurrent Unit(GRU).Adaptive weighted adjacency matrices are employed to encode spatial relationships among highway segments,capturing the spatiotemporal dependencies of traffic speed.Second,weather conditions and Points of Interest(POI)features are incorporated into a speed attribute enhancement unit,which expands the feature dimensionality and strengthens the repre-sentation of external factors in speed variation patterns,enabling more accurate traffic speed predic-tions.Finally,model performance is evaluated through baseline comparisons and ablation studies.Ex-perimental results indicate that dynamically integrating external attributes such as weather and POIs significantly improves both single-step and multi-step traffic speed predictions.Compared with base-line models,the proposed model reduces Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)by over 14.1%and 14.8%,respectively,for single-step prediction,and by over 4%and 14%,respectively,for multi-step prediction.In a 15-minute forecasting task,fusing both external at-tributes reduces MAE and RMSE by 23.0%and 17.8%,respectively,with weather information con-tributing more prominently and demonstrating clear complementarity between two factors.These find-ings provide valuable insights for highway speed prediction and offer guidance for improving traffic safety and intelligent highway management.
Keywords：transportation planning and managementspeed predictiondeep learningSpatio-Temporal Graph Convolutional Network(STGCN)external attribute fusion
Publication Date：2025-12-30
Online Publishing Date：2026-02-02(First online date of this platform, not the publication date of the document)
Pages：11( 126-136 )