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 …
Bike sharing usage prediction with deep learning: a survey
W Jiang - Neural Computing and Applications, 2022 - Springer
As a representative of shared mobility, bike sharing has become a green and convenient
way to travel in cities in recent years. Bike usage prediction becomes more important for …
way to travel in cities in recent years. Bike usage prediction becomes more important for …
Deep spatio-temporal residual networks for citywide crowd flows prediction
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, such as inter-region …
safety, and very challenging as it is affected by many complex factors, such as inter-region …
DNN-based prediction model for spatio-temporal data
Advances in location-acquisition and wireless communication technologies have led to
wider availability of spatio-temporal (ST) data, which has unique spatial properties (ie …
wider availability of spatio-temporal (ST) data, which has unique spatial properties (ie …
Visual analytics in urban computing: An overview
Nowadays, various data collected in urban context provide unprecedented opportunities for
building a smarter city through urban computing. However, due to heterogeneity, high …
building a smarter city through urban computing. However, due to heterogeneity, high …
Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph
Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations …
Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations …
[HTML][HTML] Predicting citywide crowd flows using deep spatio-temporal residual networks
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, including spatial …
safety, and very challenging as it is affected by many complex factors, including spatial …
Bike flow prediction with multi-graph convolutional networks
One fundamental issue in managing bike sharing systems is bike flow prediction. Due to the
hardness of predicting flow for a single station, recent research often predicts flow at cluster …
hardness of predicting flow for a single station, recent research often predicts flow at cluster …
Urban flow prediction from spatiotemporal data using machine learning: A survey
Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …
public safety. This prediction task is affected by several complex and dynamic factors, such …
Flow prediction in spatio-temporal networks based on multitask deep learning
Predicting flows (eg, the traffic of vehicles, crowds, and bikes), consisting of the in-out traffic
at a node and transitions between different nodes, in a spatio-temporal network plays an …
at a node and transitions between different nodes, in a spatio-temporal network plays an …