PGSLM: Edge-enabled probabilistic graph structure learning model for traffic forecasting in Internet of vehicles
Xiaozhu Liu, Jiaru Zeng, Rongbo Zhu, Hao Liu
Abstract
With the rapid development of the 5G communications, the edge intelligence enables Internet of Vehicles (IoV) to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously. To enhance the forecasting performance, a novel edge-enabled probabilistic graph structure learning model (PGSLM) is proposed, which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network. To obtain the spatio-temporal dependencies of traffic data, the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module. During the training process, a new graph training loss is introduced, which is composed of the K nearest neighbor (KNN) graph constructed by the traffic feature tensors and the graph structure. Detailed experimental results show that, compared with existing models, the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.