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Journal of Advanced Transportation
Volume 2017, Article ID 7290248, 13 pages
Research Article

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

1Transportation and Hydrogen Systems Center, National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, CO 80401, USA
2Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA

Correspondence should be addressed to Lei Zhu; moc.liamg@7170ieluhz

Received 14 April 2017; Revised 26 June 2017; Accepted 2 July 2017; Published 16 August 2017

Academic Editor: Takahiko Kusakabe

Copyright © 2017 Lei Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.