  Taxi arrival time prediction is essential for building intelligent
transportation systems. Traditional prediction methods mainly rely on
extracting features from traffic maps, which cannot model complex situations
and nonlinear spatial and temporal relationships. Therefore, we propose
Multi-View Spatial-Temporal Model (MVSTM) to capture the mutual dependence of
spatial-temporal relations and trajectory features. Specifically, we use
graph2vec to model the spatial view, dual-channel temporal module to model the
trajectory view, and structural embedding to model traffic semantics.
Experiments on large-scale taxi trajectory data have shown that our approach is
more effective than the existing novel methods. The source code can be found at
https://github.com/775269512/SIGSPATIAL-2021-GISCUP-4th-Solution.
