Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

• Published in: IEEE transactions on intelligent transportation systems Vol. 20; no. 1; pp. 244 - 252
• Main Authors: Ashqar, Huthaifa I., Almannaa, Mohammed H., Elhenawy, Mohammed, Rakha, Hesham A., House, Leanna
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial intelligence; Bayesian analysis; cellular phone sensor data; Classification; Classifiers; Conditional probability; Feature extraction; Frequency domain analysis; Global Positioning System; Global positioning systems; GPS; hierarchical modeling; K-nearest neighbors algorithm; Machine learning; machine learning algorithms; Regression analysis; Sensors; Smartphones; Statistical analysis; Support vector machines; Time domain analysis; Transportation; Transportation mode recognition; urban computing

This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. This paper also enhances classification accuracy by extracting new frequency domain features. Many researchers have obtained these features from glo...