A Hybrid Deep Learning Approach with GCN and LSTM for Traffic ...

MF-CNN: Traffic Flow Prediction Using Convolutional Neural ...

The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic ...

A Deep Learning Framework About Traffic Flow Forecasting for ...

Zhu et al. proposed a new traffic flow prediction method based on RNN-GCN and the Belief Rule Base (BRB) (32). Spatial Temporal Graph ...

Deep Learning Algorithms for Traffic Forecasting: A Comprehensive ...

Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate ...

Road traffic can be predicted by machine learning equally effectively ...

One of the many examples of this approach is a publication, whose authors propose a combination of CNN and LSTM (Conv-LSTM) methods for ...

Multi-Head Spatiotemporal Attention Graph Convolutional Network ...

In a bid to improve neural networks' capability to predict traffic forecasts accurately, attention models are often developed to create a vector ...

WEST GCN-LSTM: Weighted Stacked Spatio-Temporal ... - arXiv

Towards achieving this goal, we extend spatio-temporal graph neural networks in a manner that is aligned with the regional traffic forecasting ...

A New Way of Airline Traffic Prediction Based on GCN-LSTM

However, the above method based on machine learning is based on the historical data of each air station forecast, this method does not take ...

Short-Term Traffic Flow Prediction Based on Graph Convolutional ...

In this paper, a deep learning model, GCN-LSTM (graph convolutional network-LSTM), was proposed with encoder and decoder structure. GCN-LSTM ...

stellargraph/demos/time-series/gcn-lstm-time-series.ipynb at develop

The architecture of the GCN-LSTM model is inspired by the paper: T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. The authors have made ...

Traffic flow prediction models – A review of deep learning techniques

Furthermore, SAEs do not require a labeled training set, being an unsupervised learning method, and thus, they can easily handle mislabeled data. However, they ...

A Hybrid GCN-LSTM Model for Driver Drowsiness Detection

Although the application of convolutional neural networks has brought about great progress in this field, they do not perform well in complex driving scenarios ...

LSTM network: a deep learning approach for short-term traffic forecast

provided a general review on traffic flow prediction with big data, and proposed a deep learning approach, in which a stacked auto-encoder (SAE) ...

Graph Neural Networks for Traffic Prediction - page for sungsoo blog

A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB[J]. ... Using a Hybrid Machine Learning-Based Model, Ad Hoc Networks, 2020.

Hybrid Deep Learning Approach for Traffic Speed Prediction | Big Data

Second, the temporal layer employs convolutional long short-term memory (ConvLSTM) networks to model closeness, daily periodicity, and weekly periodicity in the ...

Traffic forecasting using graph neural networks and LSTM - Keras

One popular method to solve this problem is to consider each road segment's traffic speed as a separate timeseries and predict the future values ...

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Finally,. KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open. Road ...

A Deep Learning Approach for Forecasting Air Pollution in South ...

Using LSTM and GRU neural network methods for traffic ... T-GCN ... Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning ...

Distributed Multi-Intersection Traffic Flow Prediction using Deep ...

Shafiq, “Applying Hybrid Lstm-Gru Model. Based on Heterogeneous Data ... Jiang, “A hybrid deep learning based traffic flow prediction method and its ...