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.
@article{arxiv.2109.07402,
title = {Multi View Spatial-Temporal Model for Travel Time Estimation},
author = {ZiChuan Liu and Zhaoyang Wu and Meng Wang and Rui Zhang},
journal= {arXiv preprint arXiv:2109.07402},
year = {2021}
}