WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting

arXiv:2405.00570v1 Announce Type: new
Abstract: Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims at expanding upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions, as well as the populations that traverse them, …

abstract applications art arxiv challenge challenges context cs.ai cs.lg everything fields forecasting graph graph neural networks internet lstm mobility networks neural networks regional results state temporal traffic type urban west work