Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction
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초록
The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methodsare weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structuresand insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flowprediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that isdesigned to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graphoptimization module is used to model the dynamic road network structure. The experimental evaluationconducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all timeintervals.
Abstract
The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methodsare weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structuresand insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flowprediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that isdesigned to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graphoptimization module is used to model the dynamic road network structure. The experimental evaluationconducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all timeintervals.
참고문헌 (11)
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