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Multi-Scale Spatio-Temporal Attention Networks; Network-Scale Traffic Learning and Forecasting
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DOI：10.3390/s24175543.png)
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Abstract
En 中文
Accurate and timely;
ecasting of traffic on local road networks is crucial;
deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored;
learning and;
ecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is per;
med. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic;
ecasting benchmarks on two large-scale real-world traffic datasets.
Keywords:
traffic
ecasting
spatio-temporal networks
graph convolutional networks
multi-dimensional lstm
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