Work

Gct-TTE: graph convolutional transformer for travel time estimation

Year: 2024

Type: article

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.

Source: Journal Of Big Data

DOI: 10.1186/s40537-023-00841-1

Authors: Vladimir Mashurov, Vaagn Chopuryan, Vadim Porvatov, Arseny Ivanov, Natalia Semenova

Institutions: National University of Science and Technology, AIRI - Artificial Intelligence Research Institute

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,090