[HTML][HTML] A review of spatially-explicit GeoAI applications in Urban Geography
P Liu, F Biljecki - International Journal of Applied Earth Observation and …, 2022 - Elsevier
Urban Geography studies forms, social fabrics, and economic structures of cities from a
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban …
Traffic prediction using artificial intelligence: review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
Machine learning-based traffic prediction models for intelligent transportation systems
A Boukerche, J Wang - Computer Networks, 2020 - Elsevier
Abstract Intelligent Transportation Systems (ITS) have attracted an increasing amount of
attention in recent years. Thanks to the fast development of vehicular computing hardware …
attention in recent years. Thanks to the fast development of vehicular computing hardware …
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
T Bogaerts, AD Masegosa, JS Angarita-Zapata… - … Research Part C …, 2020 - Elsevier
Traffic forecasting is an important research area in Intelligent Transportation Systems that is
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep …
An effective spatial-temporal attention based neural network for traffic flow prediction
Due to its importance in Intelligent Transport Systems (ITS), traffic flow prediction has been
the focus of many studies in the last few decades. Existing traffic flow prediction models …
the focus of many studies in the last few decades. Existing traffic flow prediction models …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Deep learning for road traffic forecasting: Does it make a difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …
This has also been the case of Intelligent Transportation Systems, in which several areas …
Spatial–temporal complex graph convolution network for traffic flow prediction
Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction
Traffic flow prediction based on vehicle trajectories collected from the installed GPS devices
is critically important to Intelligent Transportation Systems (ITS). One limitation of existing …
is critically important to Intelligent Transportation Systems (ITS). One limitation of existing …
A graph deep learning method for short‐term traffic forecasting on large road networks
Short‐term traffic flow prediction on a large‐scale road network is challenging due to the
complex spatial–temporal dependencies, the directed network topology, and the high …
complex spatial–temporal dependencies, the directed network topology, and the high …