Inferring Transportation Mode using pooled features from time and frequency domains

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Muhammad, AR (Autor) Outra; Ana Aguiar (Autor) FEUP; Mendes Moreira, J (Autor) Outra

Ata de Conferência Internacional

Título: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Páginas: 3985-3990

26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023

Bilbao, 24 September 2023 through 28 September 2023

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-010-346

DOI: 10.1109/itsc57777.2023.10422399

Abstract (EN): Identifying the types of transportation modes that people use is a central problem in transportation research. Effective feature construction plays a crucial role in developing a successful machine learning model. In this study, we demonstrate an approach to identify commuters' transportation modes solely using raw GPS trajectory data. First, we transform the representation of location data points into a vector of motion features in the time domain. Next, we create fixed-length instances in the time domain. Subsequently, we transform the instances time-domain features into frequency-domain features using the fast Fourier transform. This results in a pool of features for the instances in both the time and frequency domains. We use the Sequential Forward Floating Selection technique to select the most informative features to train our models. We evaluate our approach using two distinct real-world GPS trajectory datasets. Our results show that the random forest classifier achieved an ROC-AUC scores of 79% and 89% on the respective datasets.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6