Gct-TTE: graph convolutional transformer for travel time estimation

被引:1
|
作者
Mashurov, Vladimir [1 ]
Chopuryan, Vaagn [1 ]
Porvatov, Vadim [1 ,2 ]
Ivanov, Arseny [2 ]
Semenova, Natalia [1 ,3 ]
机构
[1] PJSC Sberbank, Vavilova St, Moscow 117312, Russia
[2] Natl Univ Sci & Technol MISiS, Lenin Ave 4, Moscow 119049, Russia
[3] Artificial Intelligence Res Inst, Nizhny Susalny Lane 5, Moscow 105064, Russia
关键词
Machine learning; Graph convolutional networks; Transformers; Geospatial data; Travel time estimation;
D O I
10.1186/s40537-023-00841-1
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
This paper introduces a new transformer-based model for the problem of travel time estimation. The key feature of the proposed GCT-TTE architecture is the utilization of different data modalities capturing different properties of an input path. Along with the extensive study regarding the model configuration, we implemented and evaluated a sufficient number of actual baselines for path-aware and path-blind settings. The conducted computational experiments have confirmed the viability of our pipeline, which outperformed state-of-the-art models on both considered datasets. Additionally, GCT-TTE was deployed as a web service accessible for further experiments with user-defined routes.
引用
收藏
页数:14
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