Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0234003 |
| _version_ | 1819111570706268160 |
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| author | Andreas Bjerre-Nielsen Kelton Minor Piotr Sapieżyński Sune Lehmann David Dreyer Lassen |
| author_facet | Andreas Bjerre-Nielsen Kelton Minor Piotr Sapieżyński Sune Lehmann David Dreyer Lassen |
| author_sort | Andreas Bjerre-Nielsen |
| collection | DOAJ |
| description | Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications. |
| first_indexed | 2024-12-22T03:59:43Z |
| format | Article |
| id | doaj.art-367f289d53c546d08200d86bd30c0201 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T03:59:43Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-367f289d53c546d08200d86bd30c02012022-12-21T18:39:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01157e023400310.1371/journal.pone.0234003Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.Andreas Bjerre-NielsenKelton MinorPiotr SapieżyńskiSune LehmannDavid Dreyer LassenUnderstanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.https://doi.org/10.1371/journal.pone.0234003 |
| spellingShingle | Andreas Bjerre-Nielsen Kelton Minor Piotr Sapieżyński Sune Lehmann David Dreyer Lassen Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. PLoS ONE |
| title | Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. |
| title_full | Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. |
| title_fullStr | Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. |
| title_full_unstemmed | Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. |
| title_short | Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. |
| title_sort | inferring transportation mode from smartphone sensors evaluating the potential of wi fi and bluetooth |
| url | https://doi.org/10.1371/journal.pone.0234003 |
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