  Travel time estimation is one of the core tasks for the development of
intelligent transportation systems. Most previous works model the road segments
or intersections separately by learning their spatio-temporal characteristics
to estimate travel time. However, due to the continuous alternations of the
road segments and intersections in a path, the dynamic features are supposed to
be coupled and interactive. Therefore, modeling one of them limits further
improvement in accuracy of estimating travel time. To address the above
problems, a novel graph-based deep learning framework for travel time
estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural
Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise
graphs to respectively characterize the adjacency relations of intersections
and that of road segments. In order to extract the joint spatio-temporal
correlations of the intersections and road segments, we adopt the
spatio-temporal dual graph learning approach that incorporates multiple
spatial-temporal dual graph learning modules with multi-scale network
architectures for capturing multi-level spatial-temporal information from the
dual graph. Finally, we employ the multi-task learning approach to estimate the
travel time of a given whole route, each road segment and intersection
simultaneously. We conduct extensive experiments to evaluate our proposed model
on three real-world trajectory datasets, and the experimental results show that
STDGNN significantly outperforms several state-of-art baselines.
