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 …
[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Eta prediction with graph neural networks in google maps
A Derrow-Pinion, J She, D Wong, O Lange… - Proceedings of the 30th …, 2021 - dl.acm.org
Travel-time prediction constitutes a task of high importance in transportation networks, with
web mapping services like Google Maps regularly serving vast quantities of travel time …
web mapping services like Google Maps regularly serving vast quantities of travel time …
A hybrid visualization model for knowledge mapping: Scientometrics, SAOM, and SAO
Predicting the crowd flow in various areas of the city is of strategic importance for traffic
control and public safety. In recent years, crowd flow prediction based on spatio-temporal …
control and public safety. In recent years, crowd flow prediction based on spatio-temporal …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
Understanding private car aggregation effect via spatio-temporal analysis of trajectory data
Understanding the private car aggregation effect is conducive to a broad range of
applications, from intelligent transportation management to urban planning. However, this …
applications, from intelligent transportation management to urban planning. However, this …
A survey on graph representation learning methods
S Khoshraftar, A An - ACM Transactions on Intelligent Systems and …, 2024 - dl.acm.org
Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …
goal of graph representation learning is to generate graph representation vectors that …
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …
intelligent transportation systems, which is the foundation of route planning and traffic …
Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning
With the increasing adoption of GPS modules, there are a wide range of urban applications
based on trajectory data analysis, such as vehicle navigation, travel time estimation, and …
based on trajectory data analysis, such as vehicle navigation, travel time estimation, and …