How are you Riding? Transportation Mode Identification from Raw GPS Data

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Thiago Andrade Silva (Autor) Outra; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31 - September 2, 2022, Proceedings

Páginas: 648-659

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-EGH

DOI: 10.1007/978-3-031-16474-3_53

Abstract (EN): Analyzing the way individuals move is fundamental to understand the dynamics of humanity. Transportation mode plays a significant role in human behavior as it changes how individuals travel, how far, and how often they can move. The identification of transportation modes can be used in many applications and it is a key component of the internet of things (IoT) and the Smart Cities concept as it helps to organize traffic control and transport management. In this paper, we propose the use of ensemble methods to infer the transportation modes using raw GPS data. From latitude, longitude, and timestamp we perform feature engineering in order to obtain more discriminative fields for the classification. We test our features in several machine learning algorithms and among those with the best results we perform feature selection using the Boruta method in order to boost our accuracy results and decrease the amount of data, processing time, and noise in the model. We assess the validity of our approach on a real-world dataset with several different transportation modes and the results show the efficacy of our approach.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12