A traffic pattern detection algorithm based on multimodal sensing
Nowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718807832 |
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| _version_ | 1849704403417694208 |
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| author | Yanjun Qin Haiyong Luo Fang Zhao Zhongliang Zhao Mengling Jiang |
| author_facet | Yanjun Qin Haiyong Luo Fang Zhao Zhongliang Zhao Mengling Jiang |
| author_sort | Yanjun Qin |
| collection | DOAJ |
| description | Nowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode detection. Using the transportation mode detection, results can increase commuting efficiency and optimize metropolitan transportation planning. Although much work has been done on transportation mode detection problem, the accuracy is not sufficient. In this article, an accurate traffic pattern detection algorithm based on multimodal sensing is proposed. This algorithm first extracts various sensory features and semantic features from four types of sensor (i.e. accelerator, gyroscope, magnetometer, and barometer). These sensors are commonly embedded in commodity smartphones. All the extracted features are then fed into a convolutional neural network to infer traffic patterns. Extensive experimental results show that the proposed scheme can identify four transportation patterns with 94.18% accuracy. |
| format | Article |
| id | doaj-art-03cd8158f5f74cb0a36673e7baf8f185 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2018-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-03cd8158f5f74cb0a36673e7baf8f1852025-08-20T03:16:46ZengWileyInternational Journal of Distributed Sensor Networks1550-14772018-10-011410.1177/1550147718807832A traffic pattern detection algorithm based on multimodal sensingYanjun Qin0Haiyong Luo1Fang Zhao2Zhongliang Zhao3Mengling Jiang4Beijing University of Posts and Telecommunications, Beijing, ChinaInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaInstitute of Computer Science, University of Bern, Bern, SwitzerlandBeijing University of Posts and Telecommunications, Beijing, ChinaNowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode detection. Using the transportation mode detection, results can increase commuting efficiency and optimize metropolitan transportation planning. Although much work has been done on transportation mode detection problem, the accuracy is not sufficient. In this article, an accurate traffic pattern detection algorithm based on multimodal sensing is proposed. This algorithm first extracts various sensory features and semantic features from four types of sensor (i.e. accelerator, gyroscope, magnetometer, and barometer). These sensors are commonly embedded in commodity smartphones. All the extracted features are then fed into a convolutional neural network to infer traffic patterns. Extensive experimental results show that the proposed scheme can identify four transportation patterns with 94.18% accuracy.https://doi.org/10.1177/1550147718807832 |
| spellingShingle | Yanjun Qin Haiyong Luo Fang Zhao Zhongliang Zhao Mengling Jiang A traffic pattern detection algorithm based on multimodal sensing International Journal of Distributed Sensor Networks |
| title | A traffic pattern detection algorithm based on multimodal sensing |
| title_full | A traffic pattern detection algorithm based on multimodal sensing |
| title_fullStr | A traffic pattern detection algorithm based on multimodal sensing |
| title_full_unstemmed | A traffic pattern detection algorithm based on multimodal sensing |
| title_short | A traffic pattern detection algorithm based on multimodal sensing |
| title_sort | traffic pattern detection algorithm based on multimodal sensing |
| url | https://doi.org/10.1177/1550147718807832 |
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