Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices and Built Environment
| 项目名称： | Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices and Built Environment |
| 摘要： | The relationship between transportation and health may play a significant role in improving the public�s wellbeing due to physical activities and health benefits of active transportation. Travel behavior researchers need to investigate the relationship between transportation mode choices and human health by observing traveler behaviors and their effect on physical activity and public health. This research identifies and categorizes the health outcomes from daily physical activity and daily travel activities by employing wearable devices with sensing and GPS tracking technology. In this study, the research team develops an integrated data collection and processing platform named �PASTA� to monitor the participant�s daily travel and physical activities. This platform automates data collection and integrates the big data processing of daily travel GPS trajectories from a mobile application designed by the research team with physical activity data from the Fitbit Charge 2/3. The study collects data from a total of 120 participants from Kalamazoo, MI, and Arlington, TX for a 6-month period. The PASTA platform requires many different analyses to observe the transportation impact on health outcomes; these include user activity/trip recognition and transportation mode detection. The study also explores the association between physical activity and an individual�s socioeconomic-body composition profile and proposes fusion into an integrated transportation and health impact model (ITHIM). Arlington participants use more active transportation (bicycle and walking) than Kalamazoo participants who use public transit more frequently. For activity/trip recognition, the study observes the highest accuracy for the combined Geohash-GIS approach with a dwell time of 5 minutes. The Random Forest model outperforms the other machine learning models in terms of transportation mode detection. The participant�s BMI, age, gender, and baseline active travel time show a direct effect on the transportation-related physical activity level. This research provides a method to quantify the amount of physical activity and health benefits associated with transportation activity. |
| 状态： | Completed |
| 资金： | 129969 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | Transportation Research Center for Livable Communities |
| 项目负责人： | Dunn, Denise E |
| 执行机构： | Western Michigan University |
| 主要研究人员： | Hyun, Kate |
| 开始时间： | 20170815 |
| 预计完成日期： | 20190830 |
| 实际结束时间： | 20190830 |
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