A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
A general traffic flow prediction approach based on spatial-temporal graph attention
C Tang, J Sun, Y Sun, M Peng, N Gan - IEEE Access, 2020 - ieeexplore.ieee.org
Accurate and reliable traffic flow prediction is critical to the safe and stable deployment of
intelligent transportation systems. However, it is very challenging since the complex spatial …
intelligent transportation systems. However, it is very challenging since the complex spatial …
Global spatial-temporal graph convolutional network for urban traffic speed prediction
L Ge, S Li, Y Wang, F Chang, K Wu - Applied Sciences, 2020 - mdpi.com
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However,
due to the complex spatial-temporal correlations of traffic data, it is very challenging to …
due to the complex spatial-temporal correlations of traffic data, it is very challenging to …
Graph attention temporal convolutional network for traffic speed forecasting on road networks
Traffic speed forecasting plays an increasingly essential role in successful intelligent
transportation systems. However, this still remains a challenging task when the accuracy …
transportation systems. However, this still remains a challenging task when the accuracy …
A novel residual graph convolution deep learning model for short-term network-based traffic forecasting
Short-term traffic forecasting on large street networks is significant in transportation and
urban management, such as real-time route guidance and congestion alleviation …
urban management, such as real-time route guidance and congestion alleviation …
Adaptive spatiotemporal inceptionnet for traffic flow forecasting
Traffic flow forecasting is crucial to Intelligent Transportation Systems (ITS), particularly for
route planning and traffic management. Spatiotemporal graph neural networks have been …
route planning and traffic management. Spatiotemporal graph neural networks have been …
Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
The traffic state in an urban transportation network is determined via spatio-temporal traffic
propagation. In early traffic forecasting studies, time-series models were adopted to …
propagation. In early traffic forecasting studies, time-series models were adopted to …
Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting
Traffic flow prediction plays an important role in many spatial-temporal data applications, eg,
traffic management and urban planning. Various deep learning techniques are developed to …
traffic management and urban planning. Various deep learning techniques are developed to …
Adaptive spatio-temporal graph neural network for traffic forecasting
Accurate traffic forecasting is of vital importance for the management and decision in
intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic …
intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic …