Inferring Transportation Mode using pooled features from time and frequency domains

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Muhammad, AR (Author) Other; Ana Aguiar (Author) FEUP; Mendes Moreira, J (Author) Other

Conference proceedings International

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

Pages: 3985-3990

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

Bilbao, 24 September 2023 through 28 September 2023

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6