A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Deep spatio-temporal 3D dilated dense neural network for traffic flow prediction
R He, C Zhang, Y Xiao, X Lu, S Zhang, Y Liu - Expert Systems with …, 2024 - Elsevier
Traffic flow prediction is increasingly vital for the administration of metropolitan areas. Many
research on spatio-temporal networks have been explored but the impacts of both spatial …
research on spatio-temporal networks have been explored but the impacts of both spatial …
Spatio-temporal fusion graph convolutional network for traffic flow forecasting
In most recent research, the traffic forecasting task is typically formulated as a spatio-
temporal graph modeling problem. For spatial correlation, they typically learn the shared …
temporal graph modeling problem. For spatial correlation, they typically learn the shared …
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as
transportation management, resource optimization, and urban planning. Although pretrained …
transportation management, resource optimization, and urban planning. Although pretrained …
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
Multi-level Graph Memory Network Cluster Convolutional Recurrent Network for traffic forecasting
L Sun, W Dai, G Muhammad - Information Fusion, 2024 - Elsevier
Traffic forecasting plays a vital role in the management of urban road networks and the
development of intelligent transportation systems. To effectively capture spatial and temporal …
development of intelligent transportation systems. To effectively capture spatial and temporal …
A multi-directional recurrent graph convolutional network model for reconstructing traffic spatiotemporal diagram
ABSTRACT The Time Space Diagram (TSD) can abstractly represent multiple data sources
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
and the macroscopic state of road traffic. However, the TSDs may be incomplete due to …
Metro Station functional clustering and dual-view recurrent graph convolutional network for metro passenger flow prediction
The metro system is indispensable for alleviating traffic congestion in the urban
transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring …
transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring …
Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network
Y Zhu, B Jiang, H Jin, M Zhang, F Gao… - ACM Transactions on …, 2024 - dl.acm.org
A networked time series (NETS) is a family of time series on a given graph, one for each
node. It has a wide range of applications from intelligent transportation to environment …
node. It has a wide range of applications from intelligent transportation to environment …
CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting
Spatiotemporal traffic forecasting plays a critical role in intelligent transportation systems,
which empowers diverse urban services. Existing traffic forecasting frameworks usually …
which empowers diverse urban services. Existing traffic forecasting frameworks usually …