Gct-TTE: graph convolutional transformer for travel time estimation

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Big Data Pub Date : 2024-01-13 DOI:10.1186/s40537-023-00841-1
Vladimir Mashurov, Vaagn Chopuryan, Vadim Porvatov, Arseny Ivanov, Natalia Semenova
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Abstract

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.

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Gct-TTE:用于旅行时间估算的图卷积变换器
本文针对旅行时间估算问题介绍了一种基于变压器的新模型。建议的 GCT-TTE 架构的主要特点是利用不同的数据模式捕捉输入路径的不同属性。在对模型配置进行广泛研究的同时,我们还针对路径感知和路径盲设置实施并评估了足够数量的实际基线。所进行的计算实验证实了我们管道的可行性,它在所考虑的两个数据集上的表现都优于最先进的模型。此外,GCT-TTE 被部署为网络服务,可用于用户自定义路径的进一步实验。
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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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