Using Smart Phone Sensors to Detect Transportation Modes
The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation mode...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/14/11/20843 |
| _version_ | 1828112715605868544 |
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| author | Hao Xia Yanyou Qiao Jun Jian Yuanfei Chang |
| author_facet | Hao Xia Yanyou Qiao Jun Jian Yuanfei Chang |
| author_sort | Hao Xia |
| collection | DOAJ |
| description | The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users. |
| first_indexed | 2024-04-11T11:54:52Z |
| format | Article |
| id | doaj.art-08d22aa44e4f4b4e89b061e2691e010e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:54:52Z |
| publishDate | 2014-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-08d22aa44e4f4b4e89b061e2691e010e2022-12-22T04:25:11ZengMDPI AGSensors1424-82202014-11-011411208432086510.3390/s141120843s141120843Using Smart Phone Sensors to Detect Transportation ModesHao Xia0Yanyou Qiao1Jun Jian2Yuanfei Chang3The Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, ChinaThe Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, ChinaThe Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, ChinaThe Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, ChinaThe proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users.http://www.mdpi.com/1424-8220/14/11/20843transportation mode classificationbuilt-in sensorsmart phonetrajectory |
| spellingShingle | Hao Xia Yanyou Qiao Jun Jian Yuanfei Chang Using Smart Phone Sensors to Detect Transportation Modes Sensors transportation mode classification built-in sensor smart phone trajectory |
| title | Using Smart Phone Sensors to Detect Transportation Modes |
| title_full | Using Smart Phone Sensors to Detect Transportation Modes |
| title_fullStr | Using Smart Phone Sensors to Detect Transportation Modes |
| title_full_unstemmed | Using Smart Phone Sensors to Detect Transportation Modes |
| title_short | Using Smart Phone Sensors to Detect Transportation Modes |
| title_sort | using smart phone sensors to detect transportation modes |
| topic | transportation mode classification built-in sensor smart phone trajectory |
| url | http://www.mdpi.com/1424-8220/14/11/20843 |
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