A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network.pdf

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A Deep Learning Approach for Long
Term Traffic Flow Prediction With
Multifactor Fusion Using Spatiotemporal
Graph Convolutional Network
Abstract
As a vital research subject in the field of intelligent transportation systems
(ITSs), traffic flow prediction using deep learning methods has attracted much
attention in recent years. However, numerous existing studies mainly focus on
short-term traffic flow predictions and fail to consider the influence of external
factors. Effective long-term traffic flow prediction has become a challenging
issue. As a solution to these challenges, this paper proposes a deep learning
approach based on a spatiotemporal gr
term traffic flow prediction with multiple factors. In the proposed method, our
innovative idea is to introduce an attribute feature unit (AF
external factors into a spatiotemporal graph convolutional networ
proposed method consists of (1) constructing a weighted adjacency matrix
using Gaussian similarity functions; (2) assembling a feature matrix to store
time-series traffic flow; (3) building an external attribute matrix composed of
external factors, including temperature, visibility, and weather conditions; and
A Deep Learning Approach for Long
Term Traffic Flow Prediction With
Multifactor Fusion Using Spatiotemporal
Graph Convolutional Network
As a vital research subject in the field of intelligent transportation systems
(ITSs), traffic flow prediction using deep learning methods has attracted much
attention in recent years. However, numerous existing studies mainly focus on
low predictions and fail to consider the influence of external
term traffic flow prediction has become a challenging
issue. As a solution to these challenges, this paper proposes a deep learning
approach based on a spatiotemporal graph convolutional network for long
term traffic flow prediction with multiple factors. In the proposed method, our
innovative idea is to introduce an attribute feature unit (AF-
external factors into a spatiotemporal graph convolutional networ
proposed method consists of (1) constructing a weighted adjacency matrix
using Gaussian similarity functions; (2) assembling a feature matrix to store
series traffic flow; (3) building an external attribute matrix composed of
including temperature, visibility, and weather conditions; and
A Deep Learning Approach for Long-
Term Traffic Flow Prediction With
Multifactor Fusion Using Spatiotemporal
As a vital research subject in the field of intelligent transportation systems
(ITSs), traffic flow prediction using deep learning methods has attracted much
attention in recent years. However, numerous existing studies mainly focus on
low predictions and fail to consider the influence of external
term traffic flow prediction has become a challenging
issue. As a solution to these challenges, this paper proposes a deep learning
aph convolutional network for long-
term traffic flow prediction with multiple factors. In the proposed method, our
-unit) to fuse
external factors into a spatiotemporal graph convolutional network. The
proposed method consists of (1) constructing a weighted adjacency matrix
using Gaussian similarity functions; (2) assembling a feature matrix to store
series traffic flow; (3) building an external attribute matrix composed of
including temperature, visibility, and weather conditions; and

(4) building a spatiotemporal graph convolutional network based on a deep
learning architecture (i.e., T-GCN). The experimental results indicate that (1)
the performance of our method considering spatiotemporal dependence has
better prediction capability than baseline models; (2) the fusion of
meteorological factors can reduce the inaccuracy of traffic prediction; and (3)
our method has high accuracy and stability in long-term traffic flow prediction.

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