Dual dynamic spatial-temporal graph convolution network for traffic prediction
Recently, Graph Convolution Network (GCN) and Temporal Convolution Network (TCN) are
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
introduced into traffic prediction and achieve state-of-the-art performance due to their good …
Multitask hypergraph convolutional networks: A heterogeneous traffic prediction framework
J Wang, Y Zhang, L Wang, Y Hu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Traffic prediction methods on a single-source data have achieved excellent results in recent
years, especially the Graph Convolutional Networks (GCN) based models with spatio …
years, especially the Graph Convolutional Networks (GCN) based models with spatio …
An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD
Traffic density estimation is a very important component of an automated traffic monitoring
system. Traffic density estimation can be used in a number of traffic applications–from …
system. Traffic density estimation can be used in a number of traffic applications–from …
Real-time traffic speed estimation with graph convolutional generative autoencoder
Real-time traffic speed estimation is an essential component of intelligent transportation
system (ITS) technologies. It is the foundation of modern transportation control and …
system (ITS) technologies. It is the foundation of modern transportation control and …
Hybrid dual Kalman filtering model for short‐term traffic flow forecasting
Short‐term traffic flow forecasting is a fundamental and challenging task since it is required
for the successful deployment of intelligent transportation systems and the traffic flow is …
for the successful deployment of intelligent transportation systems and the traffic flow is …
[HTML][HTML] RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction
Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
cities. Travelers as well as urban managers rely on reliable traffic information to make their …
Data fusion for multi-source sensors using GA-PSO-BP neural network
J Liu, J Huang, R Sun, H Yu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The development of real-time road condition systems will better monitor road network
operation status. However, the weak point of all these systems is their need for …
operation status. However, the weak point of all these systems is their need for …
Traffic density estimation in vehicular ad hoc networks: A review
Abstract Nowadays, vehicular Ad hoc Networks (VANETs) are gaining enormous research
interest. Even though the leading reason for developing VANETs is traffic safety, many …
interest. Even though the leading reason for developing VANETs is traffic safety, many …
Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting
Traffic forecasting is highly challenging due to its complex spatial and temporal
dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been …
dependencies in the traffic network. Graph Convolutional Neural Network (GCN) has been …
Semantics-aware dynamic graph convolutional network for traffic flow forecasting
Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …