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
相关论文
共 50 条
  • [31] Triplet-contrastive Periodical Siamese Graph Networks for Travel Time Estimation
    Elsir, Alfateh M. Tag
    Khaled, Alkilane
    Shen, Yanming
    Proceedings of the International Joint Conference on Neural Networks, 2023, 2023-June
  • [32] Spatio-temporal Dual Graph Neural Networks for Travel Time Estimation
    Jin, Guangyin
    Yan, Huan
    Li, Fuxian
    Huang, Jincai
    Li, Yong
    ACM Transactions on Spatial Algorithms and Systems, 2024, 10 (03)
  • [33] HLGST: Hybrid local-global spatio-temporal model for travel time estimation using Siamese graph convolutional with triplet networks
    Elsir, Alfateh M. Tag
    Khaled, Alkilane
    Shen, Yanming
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 229
  • [34] Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder
    Yu, Jamen Jian Qiao
    Gu, Jiatao
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 20 (10) : 3940 - 3951
  • [35] MCAGCN: Multi-component attention graph convolutional neural network for road travel time prediction
    Zhao, Zhihua
    Li, Chao
    Zhang, Xue
    Xie, Nengfu
    Zeng, Qingtian
    IET INTELLIGENT TRANSPORT SYSTEMS, 2024, 18 (01) : 139 - 153
  • [36] Transformer-Based Graph Convolutional Network for Sentiment Analysis
    AlBadani, Barakat
    Shi, Ronghua
    Dong, Jian
    Al-Sabri, Raeed
    Moctard, Oloulade Babatounde
    APPLIED SCIENCES-BASEL, 2022, 12 (03):
  • [37] Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling
    Corrias, Riccardo
    Gjoreski, Martin
    Langheinrich, Marc
    SENSORS, 2023, 23 (10)
  • [38] A Concurrent Fault Diagnosis Method of Transformer Based on Graph Convolutional Network and Knowledge Graph
    Liu, Liqing
    Wang, Bo
    Ma, Fuqi
    Zheng, Quan
    Yao, Liangzhong
    Zhang, Chi
    Mohamed, Mohamed A.
    FRONTIERS IN ENERGY RESEARCH, 2022, 10
  • [39] Estimation of time-series on graphs using Bayesian graph convolutional neural networks
    Teimury, Fatemeh
    Pal, Soumyasundar
    Amini, Arezou
    Coates, Mark
    WAVELETS AND SPARSITY XVIII, 2019, 11138
  • [40] Graph-Time Convolutional Autoencoders
    Sabbaqi, Mohammad
    Taormina, Riccardo
    Hanjalic, Alan
    Isufi, Elvin
    LEARNING ON GRAPHS CONFERENCE, VOL 198, 2022, 198