Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning approach
Xudong Qi, Junfeng Yao, Ping Wang, Tongtong Shi, Yajie Zhang, Xiangmo Zhao
Abstract
Abstract Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.
Topics & Concepts
Computer scienceTraffic flow (computer networking)GraphIntelligent transportation systemConvolutional neural networkDeep learningArtificial intelligenceData miningComponent (thermodynamics)FusionFlow networkFeature (linguistics)Machine learningTransport engineeringEngineeringMathematicsTheoretical computer scienceComputer securityPhysicsPhilosophyMathematical optimizationThermodynamicsLinguisticsTraffic Prediction and Management TechniquesTransportation Planning and OptimizationTraffic control and management