Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel

Abstract

Estimating Time of Travel (ETT) is a crucial element of intelligent transportation systems. In most previous studies, time of travel is estimated by identifying the spatio-temporal features of road segments or intersections independently. However, due to continuous changes in road segments and intersections in a path, dynamic features should be coupled and interactive. Therefore, employing only road segment or intersection features is inadequate for improving the accuracy of ETT. To address this issue, we proposed a novel deep learning framework for ETT based on a spatio-temporal heterogeneous graph neural network (STHGNN). Specifically, a heterogeneous traffic graph was first created based on intersections and road segments, which implies an adjacency correlation. Next, a learning approach for spatio-temporal heterogeneous convolutional attention networks was proposed to obtain the spatio-temporal correlations of joint intersections and road segments. This approach integrates temporal and spatial features. Finally, a fusion prediction approach was employed to estimate the travel time of a given path. Experiments were conducted on real-world path datasets to evaluate our proposed model. The results showed that STHGNN significantly outperformed the baselines.