3 citations · 3 across the 5 of their papers we have counts for
5 papers
GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation
Vladimir Mashurov, Vaagn Chopurian, Vadim Porvatov +2
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…
Revising deep learning methods in parking lot occupancy detection
Anastasia Martynova, Mikhail Kuznetsov, Vadim Porvatov +4
Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing dri…
5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19
Vadim Porvatov, Natalia Semenova
Automation of social network data assessment is one of the classic challenges of natural language processing. During the COVID-19 pandemic, mining people's stances from public mess…
Logistics, Graphs, and Transformers: Towards improving Travel Time Estimation
Natalia Semenova, Vadim Porvatov, Vladislav Tishin +2
The problem of travel time estimation is widely considered as the fundamental challenge of modern logistics. The complex nature of interconnections between spatial aspects of roads…
Hybrid Graph Embedding Techniques in Estimated Time of Arrival Task
Vadim Porvatov, Natalia Semenova, Andrey Chertok
Recently, deep learning has achieved promising results in the calculation of Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the start point…