Underground Train Tracking using Mobile Phone Accelerometer Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Baghoussi, Y (Autor) Outra; João Mendes-Moreira (Autor) FEUP; Moniz, N (Autor) Outra; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, Greece, September 20-23, 2020

Páginas: 1-6

23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020

20 September 2020 through 23 September 2020

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00T-ATV

DOI: 10.1109/itsc45102.2020.9294569

Abstract (EN): Location tracking is an essential problem for mobility-based applications that facilitate the daily life of Smartphone users. Existing applications often use energy-hungry sensors like GPS or gyroscope to detect significant journeys. Recent research has often focused on optimizing energy consumption. As a result, approaches were proposed using sensors fusions, hybrid or eventual sensors selection. However, such research largely neglects the performance in underground tracking of automotive mobility. Possible solutions, such as those involving barometers, have well-known issues regarding performance. Oppositely, although energy-friendly, accelerometers are often overlooked based on the assumption that pattern extraction is hard due to over-noisy characteristics of the signal. In this paper, we propose a ready-to-use Framework for underground train tracking. This Framework uses an adaptive Singular Spectrum Analysis (SSA) to process the Accelerometer data. We run an empirical study using data collected from Smartphone embedded accelerometers, to track departings and arrivals of the trains in four large European cities. Results show that: 1) the Framework is able to accurately locate the trains; 2) SSA adds improvements compared to Butterworth filters and complementary filter with sensors fusion.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6