Long-term mobile traffic forecasting using deep spatio-temporal neural networks
Forecasting with high accuracy the volume of data traffic that mobile users will consume is
becoming increasingly important for precision traffic engineering, demand-aware network …
becoming increasingly important for precision traffic engineering, demand-aware network …
Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction
With the rapid development of mobile cellular technologies and the increasing popularity of
mobile and Internet of Things (IoT) devices, timely mobile traffic forecasting with high …
mobile and Internet of Things (IoT) devices, timely mobile traffic forecasting with high …
STCNN: A spatio-temporal convolutional neural network for long-term traffic prediction
As many location-based applications provide services for users based on traffic conditions,
an accurate traffic prediction model is very significant, particularly for long-term traffic …
an accurate traffic prediction model is very significant, particularly for long-term traffic …
Deeptp: An end-to-end neural network for mobile cellular traffic prediction
The past 10 years have witnessed the rapid growth of global mobile cellular traffic demands
due to the popularity of mobile devices. While accurate traffic prediction becomes extremely …
due to the popularity of mobile devices. While accurate traffic prediction becomes extremely …
Towards spatio-temporal aware traffic time series forecasting
Traffic time series forecasting is challenging due to complex spatio-temporal dynamics-time
series from different locations often have distinct patterns; and for the same time series …
series from different locations often have distinct patterns; and for the same time series …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the
high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the …
high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the …
Domain adversarial spatial-temporal network: A transferable framework for short-term traffic forecasting across cities
Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it
serves as the cornerstone of various smart mobility applications. Though this research area …
serves as the cornerstone of various smart mobility applications. Though this research area …
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐
temporal dependencies at different scales. Recently, several hybrid deep learning models …
temporal dependencies at different scales. Recently, several hybrid deep learning models …
Unified spatio-temporal modeling for traffic forecasting using graph neural network
Research in deep learning models to forecast traffic intensities has gained great attention in
recent years due to their capability to capture the complex spatio-temporal relationships …
recent years due to their capability to capture the complex spatio-temporal relationships …