Digital twin mobility profiling: A spatio-temporal graph learning approach
With the arrival of the big data era, mobility profiling has become a viable method of utilizing
enormous amounts of mobility data to create an intelligent transportation system. Mobility …
enormous amounts of mobility data to create an intelligent transportation system. Mobility …
Metropolitan-scale Mobility Digital Twin
Mobility digital twin, which is aa virtual replica of the mobility in the physical world, is the key
building block of modern smart city applications at a metropolitan scale, including traffic …
building block of modern smart city applications at a metropolitan scale, including traffic …
Trajectory-user linking via graph neural network
Trajectory-User Linking (TUL) refers to classifying trajectories into the corresponding
generated users and has emerged as an essential spatio-temporal data mining task with a …
generated users and has emerged as an essential spatio-temporal data mining task with a …
Mining heterogeneous spatial-temporal data with graph neural network to support smart city management
F Dong - 2022 - search.proquest.com
Daily life in urban areas is challenged by the increasing population of cities with limited
resources and services. Widespread adoption of the Internet of Things, machine learning …
resources and services. Widespread adoption of the Internet of Things, machine learning …
Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems
With the advance of artificial intelligence (AI), the Internet of Things (IoT), and 5G
communication technologies, various kinds of traffic data from diverse devices can be …
communication technologies, various kinds of traffic data from diverse devices can be …
Addressing COVID-induced changes in spatiotemporal travel mobility and community structure utilizing trip data: An innovative graph-based deep learning approach
The COVID-19 pandemic has resulted in significant disruptions in mobility patterns, leading
to changes in user travel behavior. Understanding users' travel demand, travel behaviors …
to changes in user travel behavior. Understanding users' travel demand, travel behaviors …
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
To address the limitations of traffic prediction from location-bound detectors, we present
Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive …
Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive …
Traffnet: Learning causality of traffic generation for road network digital twins
Road network digital twins (RNDTs) play a critical role in the development of next-
generation intelligent transportation systems, enabling more precise traffic planning and …
generation intelligent transportation systems, enabling more precise traffic planning and …
Digital twin for transportation Big data: A reinforcement learning-based network traffic prediction approach
Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation
Systems (ITS), have received great attention in recent years. With the rapid development of …
Systems (ITS), have received great attention in recent years. With the rapid development of …
NodeSense2Vec: Spatiotemporal context-aware network embedding for heterogeneous urban mobility data
DK Chandra, J Leopold, Y Fu - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
The problem of learning latent representations of heterogeneous networks with spatial and
temporal attributes has been gaining traction in recent years, given its myriad of real-world …
temporal attributes has been gaining traction in recent years, given its myriad of real-world …