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Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Trajectory data mining: an overview
The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Urban traffic prediction from spatio-temporal data using deep meta learning
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
Reducing offloading latency for digital twin edge networks in 6G
W Sun, H Zhang, R Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
6G is envisioned to empower wireless communication and computation through the
digitalization and connectivity of everything, by establishing a digital representation of the …
digitalization and connectivity of everything, by establishing a digital representation of the …
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 …
A survey on trajectory data management, analytics, and learning
Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …
in the availability and collection of urban trajectory data, thus increasing the demand for …
[PDF][PDF] Lc-rnn: A deep learning model for traffic speed prediction.
Traffic speed prediction is known as an important but challenging problem. In this paper, we
propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction …
propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction …
[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
Predicting taxi–passenger demand using streaming data
Informed driving is increasingly becoming a key feature for increasing the sustainability of
taxi companies. The sensors that are installed in each vehicle are providing new …
taxi companies. The sensors that are installed in each vehicle are providing new …