Sep 23, 2019 · We have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features.
The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework ...
ABSTRACT Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly).
STDN is based on a spatial-temporal neural network, which handles spatial and temporal information via local CNN and LSTM, respectively. A flow-gated local CNN ...
Jun 18, 2024 · We build a study called STG4Traffic using the deep learning framework PyTorch to establish a standardized and scalable benchmark on two types of traffic ...
Sep 7, 2023 · A dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed.
Dec 30, 2023 · This paper proposes a dynamic multi-graph neural network (DMGNN) incorporating traffic accidents for multi-step traffic flow prediction.
A novel Spatial-Temporal Dynamic Network (STDN) is proposed, in which a flow gating mechanism is introduced to learn the dynamic similarity between ...
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... prediction. ... A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow. Article. Full-text available. Sep ...
... Neural Network for Traffic Flow Prediction ... A dynamic spatial-temporal deep learning framework for traffic speed prediction on large-scale road networks.