Taxi arrival time prediction is an essential part of building intelligent transportation systems. Traditional arrival time estimation methods mainly rely on traffic map feature extraction, which can not model complex situations and nonlinear spatial and temporal relationships. Therefore, we propose a Multi-View Spatial-Temporal Model (MVSTM) to capture the dependence of spatial-temporal and trajectory. Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model the traffic semantics. Experiments on large-scale taxi trajectory data show that our approach is more effective than the novel method. The source code can be obtained from https://github.com/775269512/SIGSPATIAL-2021-GISCUP-4th-Solution.

翻译：出租车抵达时间预测是建立智能交通系统的一个基本部分。传统的抵达时间估计方法主要依靠交通地图特征提取,无法模拟复杂情况和非线性空间和时间关系。因此,我们提议采用多视空间-时空模型(MVSTM)来捕捉空间-时空和轨道的依赖性。具体地说,我们用图2vec来模拟空间视角、双通道时间模块来模拟轨迹视图,以及结构嵌入交通语义模型。大规模出租车轨迹数据实验显示,我们的方法比新颖方法更有效。源代码可以从https://github.com/77526912/SIGSPAPTIAL-2021-GISCUP-4S-Soluble获得。