Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data
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 s...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/7290248 |
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| author | Lei Zhu Jeffrey Gonder Lei Lin |
| author_facet | Lei Zhu Jeffrey Gonder Lei Lin |
| author_sort | Lei Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b4f947ace7584bb496f1414e80447cf6 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b4f947ace7584bb496f1414e80447cf62025-02-03T01:11:06ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/72902487290248Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS DataLei Zhu0Jeffrey Gonder1Lei Lin2Transportation and Hydrogen Systems Center, National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, CO 80401, USATransportation and Hydrogen Systems Center, National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, CO 80401, USADepartment of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USAWith 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.http://dx.doi.org/10.1155/2017/7290248 |
| spellingShingle | Lei Zhu Jeffrey Gonder Lei Lin Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data Journal of Advanced Transportation |
| title | Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data |
| title_full | Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data |
| title_fullStr | Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data |
| title_full_unstemmed | Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data |
| title_short | Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data |
| title_sort | prediction of individual social demographic role based on travel behavior variability using long term gps data |
| url | http://dx.doi.org/10.1155/2017/7290248 |
| work_keys_str_mv | AT leizhu predictionofindividualsocialdemographicrolebasedontravelbehaviorvariabilityusinglongtermgpsdata AT jeffreygonder predictionofindividualsocialdemographicrolebasedontravelbehaviorvariabilityusinglongtermgpsdata AT leilin predictionofindividualsocialdemographicrolebasedontravelbehaviorvariabilityusinglongtermgpsdata |