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


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

Traffic flow prediction is an important functional component of Intelligent Transportation Systems (ITS). In this paper, we propose a hybrid deep learning ...

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

A hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA) to efficiently capture the spatial-temporal features ...

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

A Hybrid Deep Learning Approach with GCN and LSTM for Traffic. Flow Prediction*. Zhishuai Li, Gang Xiong, Yuanyuan Chen, Yisheng Lv†, Bin Hu, Fenghua Zhu and ...

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

The hybrid deep learning approach introduced by Yuankai.Wu et al. [25], integrating LSTM and TCN, leverages LSTM to capture long-term dependencies and TCN to ...

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

TL;DR: A hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA) to efficiently capture the spatial-temporal ...

A Hybrid Deep Learning Method for Short-Term Traffic Flow ...

Methods: This study developed a long short-term memory (LSTM) neural network optimized by the gravitational search approach (GSA) to enhance prediction accuracy ...

(PDF) A Hybrid Deep Learning Approach for Real-Time Estimation ...

By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph ...

A Hybrid Deep Learning Approach for Real-Time Estimation of ...

By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional ...

A hybrid deep learning approach for dynamic attitude and position ...

In addition to LSTM, Graph Convolutional Network (GCN) is introduced to capture the spatial relationships among the variables (Kipf & Welling, 2016). By ...

A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep ...

RNN is a classic deep learning method for handling sequence learning tasks. GRU and LSTM are most popular variants of RNN. CNN is convolutional neural networks,.

A Hybrid Deep Learning Approach for Real-Time Estimation of ...

This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway ...

Hybrid deep learning models for traffic prediction in large-scale road ...

In this paper, we focus on evaluating recent hybrid deep learning models in the task of traffic prediction.

An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic ...

The approach ensembles the long short-term memory (LSTM) neural network and the convolutional neural networks (CNN) in a parallel way. In order ...

A Hybrid Deep Learning Model for Short-Term - ProQuest

... GCN and Bi-LSTM to model the spatiotemporal dependence and periodicity of traffic data. Moreover, we design an attention layer for each component to make ...

Traffic Flow Prediction Based on Hybrid Deep Learning Models ...

To achieve imputation and prediction, we combined KNN, PMM and RNN, and GRU, LSTM and BiLSTM to achieve data estimation and traffic flow ...

A Hybrid Deep Learning Approach for Real-Time Estimation of ...

... traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning ...

A novel hybrid deep learning model with ARIMA Conv-LSTM ...

Traffic flow data has many characteristics in both time and space, like other machine learning applications. A deep learning framework, Spatiotemporal Graph ...

jwwthu/GNN4Traffic: This is the repository for the collection ... - GitHub

A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019 ...

Using LSTM and GRU neural network methods for traffic flow ...

962 Citations · Traffic Flow Prediction Using Deep Learning Models · A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction* · A Method For ...

Short-term multi-step-ahead sector-based traffic flow prediction ...

Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, ...