Design of a machine learning based mobile application with GPS, mobile sensors, public GIS: real time prediction on personal daily routes
- Authors
- 신현경
- Issue Date
- 2018
- Publisher
- 한국인터넷방송통신학회
- Keywords
- global position system(GPS); public geographic information system(GIS) data; mobile sensors; machine learning; inference rules; daily route prediction.
- Citation
- The International Journal of Advanced Smart Convergence, v.7, no.4, pp.27 - 39
- Journal Title
- The International Journal of Advanced Smart Convergence
- Volume
- 7
- Number
- 4
- Start Page
- 27
- End Page
- 39
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4583
- DOI
- 10.7236/IJASC.2018.7.4.27
- ISSN
- 2288-2847
- Abstract
- Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers’ routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning–based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 경영대학 > 금융수학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4583)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.