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
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2018.2817658

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 global positioning system data; however, this data was excluded in this paper, as the system use might deplete the smartphone's battery and signals may be lost in some areas. Our proposed two-layer framework differs from previous classification attempts in three distinct ways: 1) the outputs of the two layers are combined using Bayes' rule to choose the transportation mode with the largest posterior probability; 2) the proposed framework combines the new extracted features with traditionally used time domain features to create a pool of features; and 3) a different subset of extracted features is used in each layer based on the classified modes. Several machine learning techniques were used, including k-nearest neighbor, classification and regression tree, support vector machine, random forest, and a heterogeneous framework of random forest and support vector machine. Results show that the classification accuracy of the proposed framework outperforms traditional approaches. Transforming the time domain features to the frequency domain also adds new features in a new space and provides more control on the loss of information. Consequently, combining the time domain and the frequency domain features in a large pool and then choosing the best subset results in higher accuracy than using either domain alone. The proposed two-layer classifier obtained a maximum classification accuracy of 97.02%.