  Trajectory prediction of vehicles in city-scale road networks is of great
importance to various location-based applications such as vehicle navigation,
traffic management, and location-based recommendations. Existing methods
typically represent a trajectory as a sequence of grid cells, road segments or
intention sets. None of them is ideal, as the cell-based representation ignores
the road network structures and the other two are less efficient in analyzing
city-scale road networks. Moreover, previous models barely leverage spatial
dependencies or only consider them at the grid cell level, ignoring the
non-Euclidean spatial structure shaped by irregular road networks. To address
these problems, we propose a network-based vehicle trajectory prediction model
named NetTraj, which represents each trajectory as a sequence of intersections
and associated movement directions, and then feeds them into a LSTM
encoder-decoder network for future trajectory generation. Furthermore, we
introduce a local graph attention mechanism to capture network-level spatial
dependencies of trajectories, and a temporal attention mechanism with a sliding
context window to capture both short- and long-term temporal dependencies in
trajectory data. Extensive experiments based on two real-world large-scale taxi
trajectory datasets show that NetTraj outperforms the existing state-of-the-art
methods for vehicle trajectory prediction, validating the effectiveness of the
proposed trajectory representation method and spatiotemporal attention
mechanisms.
