A Combined Solution for Real-Time Travel Mode Detection and Trip Purpose Prediction

• Published in: IEEE transactions on intelligent transportation systems 2019-12, Vol.20 (12), p.4655-4664
• Main Authors: F. de S. Soares, Elton, Revoredo, Kate, Baiao, Fernanda, A. de M. S. Quintella, Carlos, V. Campos, Carlos Alberto
• Format: Article
• Language: English
• Subjects: Algorithms; automated machine learning; context-aware applications; Data collection; Data models; Feature extraction; Global Positioning System; Identification methods; intelligent transportation systems; knowledge discovery; Machine learning; Preprocessing; Real-time systems; Redundancy; Sensors; Smartphones; Task analysis; Travel; Travel mode detection; trip purpose prediction
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2905601

Identifying the travel modes people use on their daily commute, and the purpose of their trips, contributes to a better understanding of their mobility patterns. Therefore, the detection of the travel modes and the prediction of trips, purposes from smartphone sensors data emerged as two research problems in recent years. Most previous works have tackled these problems in separate studies, leading to an unnecessary redundancy with respect to the preprocessing steps taken. In this paper, we propose the use of a single preprocessing algorithm for both problems, using location traces obtained through smartphone sensors. The proposed technique applies multiple preprocessing steps to extract relevant features for both classification tasks and uses automated machine learning methods to identify the best classifiers and its hyperparameter configurations for each one of them. During evaluation experiments conducted with real smartphones data, a maximum accuracy of 88% for travel mode detection and 81% for trip purpose prediction were reached using our technique.