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

Abstract

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.

Authors:
ORCiD logo [1];  [1];  [2]
  1. Transportation and Hydrogen Systems Center, National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, CO 80401, USA
  2. Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, USA
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE; U.S. Department of Transportation (DOT), Federal Highway Administration (FHWA)
OSTI Identifier:
1375278
Alternate Identifier(s):
OSTI ID: 1376048
Report Number(s):
NREL/JA-5400-68857
Journal ID: ISSN 0197-6729; PII: 7290248; 7290248
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Published Article
Journal Name:
Journal of Advanced Transportation
Additional Journal Information:
Journal Name: Journal of Advanced Transportation Journal Volume: 2017; Journal ID: ISSN 0197-6729
Country of Publication:
Egypt
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; travel behavior; GPS; travel pattern; sensitivity analysis; Puget Sound Regional Council

Citation Formats

Zhu, Lei, Gonder, Jeffrey, and Lin, Lei. Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data. Egypt: N. p., 2017. Web. doi:10.1155/2017/7290248.
Zhu, Lei, Gonder, Jeffrey, & Lin, Lei. Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data. Egypt. doi:10.1155/2017/7290248.
Zhu, Lei, Gonder, Jeffrey, and Lin, Lei. Wed . "Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data". Egypt. doi:10.1155/2017/7290248.
@article{osti_1375278,
title = {Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data},
author = {Zhu, Lei and Gonder, Jeffrey and Lin, Lei},
abstractNote = {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.},
doi = {10.1155/2017/7290248},
journal = {Journal of Advanced Transportation},
number = ,
volume = 2017,
place = {Egypt},
year = {2017},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2017/7290248

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Cited by: 5 works
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