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
Y Zheng - ACM Transactions on Intelligent Systems and …, 2015 - dl.acm.org
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 …
Traffic prediction in a bike-sharing system
Bike-sharing systems are widely deployed in many major cities, providing a convenient
transportation mode for citizens' commutes. As the rents/returns of bikes at different stations …
transportation mode for citizens' commutes. As the rents/returns of bikes at different stations …
Travel time estimation of a path using sparse trajectories
In this paper, we propose a citywide and real-time model for estimating the travel time of any
path (represented as a sequence of connected road segments) in real time in a city, based …
path (represented as a sequence of connected road segments) in real time in a city, based …
Urban computing: concepts, methodologies, and applications
Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …
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 …
[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 …