Multi View Spatial-Temporal Model for Travel Time Estimation
This addresses taxi arrival time prediction for intelligent transportation systems, but appears incremental as it builds on prior methods without claiming broad SOTA.
The paper tackles taxi arrival time prediction by proposing a Multi-View Spatial-Temporal Model (MVSTM) to capture spatial-temporal and trajectory dependencies, and experiments on large-scale taxi data show it is more effective than existing methods.
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.