An OD time prediction model based on adaptive graph embedding

The accuracy of origin–destination (OD) time prediction is critical for intelligent transportation systems. To address the spatiotemporal challenges of sparse road network data, this paper introduces an OD time prediction model based on adaptive graph embedding. Firstly, to overcome the issue of limited traffic data, we present a tensor decomposition model specifically designed for estimating missing data. This model focuses on the “time slot-road segment-speed” dimension, dynamically reconstructing absent traffic trajectory data from various perspectives. Secondly, to account for the latent spatial dependencies among road network nodes, we leverage the Node2vec algorithm, which effectively uncovers weak connections in the network. An adaptive Node2vec algorithm, tailored to road network weights, is designed to effectively uncover hidden spatial correlations among adjacent intersections and neighborhoods. Simultaneously, a GRU module is developed to thoroughly explore relevant traffic flow information across distant time periods. Finally, to address the influence of dynamic and static external attributes on predicted travel time, we implement an external environment information fusion module based on the attention mechanism. This module capitalizes on the attention mechanism’s sensitivity to local features, thereby further improving the accuracy of OD time prediction. Experimental results show that the proposed model effectively captures spatiotemporal correlations, delivering superior OD time predictions compared to other baseline methods.