In this paper, we propose a novel Multi-Scale Convolutional Networks (MSCN), an end-to-end solution to solve traffic forecasting problem. MSCN first employs an ...
In this paper, we focus on analyzing the spatio-temporal features of traffic, and propose a spatio-temporal multi-scale convolutional net-work (ST-MSCN) to ...
This superposition of microscopic and macroscopic features also achieves multi-scale spatiotemporal feature capture.
Jan 15, 2021 · In this paper, we focus on analyzing the spatio-temporal features of traffic, and propose a spatio-temporal multi-scale convolutional net-work (ST-MSCN) to ...
Jan 31, 2024 · Abstract: Traffic prediction is vital to traffic planning, control, and optimization, which is necessary for intelligent traffic management.
Missing: Forecasting. | Show results with:Forecasting.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning ... - IJCAI
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Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and ...
This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured ...
However, the existing work focused on the overall traffic network instead of traffic nodes, and the latter can be useful in learning different patterns among ...
Aug 3, 2024 · The SHPGCN uses graph convolution to encode the traffic propagation features and embeds them into a bidirectional recurrent sequence for traffic ...
In general, multi- scale traffic forecast is the premise and foundation of urban traffic control and guidance, which is also one of main func- tions of the ...