Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides...
Main Authors: | Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu, Hafiz Tayyab Rauf |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/8/3836 |
Similar Items
-
Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. <i>Sensors</i> 2023, <i>23</i>, 3836
by: Ariyo Oluwasanmi, et al.
Published: (2024-12-01) -
Coordinate Attention Enhanced Adaptive Spatiotemporal Convolutional Networks for Traffic Flow Forecasting
by: Siwei Wei, et al.
Published: (2024-01-01) -
Dynamic Graph Convolution Network with Multi-head Attention for Traffic Flow Prediction
by: Hanyou DENG, et al.
Published: (2024-01-01) -
MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting
by: Yang Cao, et al.
Published: (2022-01-01) -
Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection
by: Ariyo Oluwasanmi, et al.
Published: (2021-12-01)