Travel time estimation is a basic but important part in intelligent transportation systems , especially widely applied in online map services to help travel navigation and route planning . Most previous works commonly model the road segments or intersections separately and obtain their spatial-temporal characteristics for travel timeestimation . However, due to the continuous alternation of the road segments and intersections  , the dynamic features of them 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, we propose a novel graph-based deep learning framework for travel time estimation , namely Spatial-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the spatial-temporal dual graph architecture to capture the complex correlations of both intersections and road segments. The adjacency relations of intersections and that of road segments are respectively characterized by node-wise graph and edge-wise graph . In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer that incorporates the multi-scale  spatial-temporal graph convolution networks and dual graphinteraction networks. Followed by the spatial-temporal learning layer, we also employ the multi-task learning layer to estimate the travel time of a given whole route and each road segment  simultaneously. We conduct extensive experiments to evaluate our proposed model on two real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines.
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.
R
a basic but important part in
one of the core tasks for the development of
D
, especially widely applied in online map services to help travel navigation and route planning
D
commonly
R
and obtain their spatial-temporal characteristics for travel timeestimation
by learning their spatio-temporal characteristics to estimate travel time
R
alternation
alternations
A
in a path
D
of them
D
we propose
R
, namely Spatial-Temporal
is proposed in this paper, namely Spatio-Temporal
R
spatial-temporal dual graph architecture to capture the complex correlations of both intersections and road segments. The
node-wise and edge-wise graphs to respectively characterize the
D
are respectively characterized by node-wise graph and edge-wise graph
R
capture the joint spatial-temporal dynamics
extract the joint spatio-temporal correlations
R
spatial-temporal learning layer that incorporates the
spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with
A
network architectures for capturing multi-level
R
graph convolution networks and dual graphinteraction networks. Followed by the spatial-temporal learning layer, we also
information from the dual graph. Finally, we
R
layer
approach
R
and
,
A
and intersection
R
two
three