Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes
One of the main endeavors of smart cities is the organization and subsidization of public transportation. To achieve this, it is important to obtain information about the way in which people move. This once-difficult problem can now be addressed by using smartphones. This paper introduces a machine...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9044837/ |
| id |
doaj-95aa11f5dcb746d793aab5aec2857676 |
|---|---|
| record_format |
Article |
| spelling |
doaj-95aa11f5dcb746d793aab5aec28576762021-03-30T02:56:32ZengIEEEIEEE Access2169-35362020-01-018583775839110.1109/ACCESS.2020.29822189044837Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation ModesPino Castrogiovanni0https://orcid.org/0000-0001-9080-6013Edoardo Fadda1https://orcid.org/0000-0002-5599-6349Guido Perboli2https://orcid.org/0000-0001-6900-9917Alessandro Rizzo3https://orcid.org/0000-0002-2386-3146TIM Joint Open Lab, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Torino, ItalyDipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, ItalyOne of the main endeavors of smart cities is the organization and subsidization of public transportation. To achieve this, it is important to obtain information about the way in which people move. This once-difficult problem can now be addressed by using smartphones. This paper introduces a machine learning-based framework that is able to ascertain the usage of a public or a private transportation mode by analyzing a little amount of data sampled by a user's smartphone. The presented method exhibits a good accuracy and a limited battery consumption. A public anonymized dataset based on real measurements is also provided along with this study. To the best of our knowledge, this is the first dataset of this kind that is offered to the public.https://ieeexplore.ieee.org/document/9044837/Mobilitytransportation mode detectionsmart city |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Pino Castrogiovanni Edoardo Fadda Guido Perboli Alessandro Rizzo |
| spellingShingle |
Pino Castrogiovanni Edoardo Fadda Guido Perboli Alessandro Rizzo Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes IEEE Access Mobility transportation mode detection smart city |
| author_facet |
Pino Castrogiovanni Edoardo Fadda Guido Perboli Alessandro Rizzo |
| author_sort |
Pino Castrogiovanni |
| title |
Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes |
| title_short |
Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes |
| title_full |
Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes |
| title_fullStr |
Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes |
| title_full_unstemmed |
Smartphone Data Classification Technique for Detecting the Usage of Public or Private Transportation Modes |
| title_sort |
smartphone data classification technique for detecting the usage of public or private transportation modes |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2020-01-01 |
| description |
One of the main endeavors of smart cities is the organization and subsidization of public transportation. To achieve this, it is important to obtain information about the way in which people move. This once-difficult problem can now be addressed by using smartphones. This paper introduces a machine learning-based framework that is able to ascertain the usage of a public or a private transportation mode by analyzing a little amount of data sampled by a user's smartphone. The presented method exhibits a good accuracy and a limited battery consumption. A public anonymized dataset based on real measurements is also provided along with this study. To the best of our knowledge, this is the first dataset of this kind that is offered to the public. |
| topic |
Mobility transportation mode detection smart city |
| url |
https://ieeexplore.ieee.org/document/9044837/ |
| work_keys_str_mv |
AT pinocastrogiovanni smartphonedataclassificationtechniquefordetectingtheusageofpublicorprivatetransportationmodes AT edoardofadda smartphonedataclassificationtechniquefordetectingtheusageofpublicorprivatetransportationmodes AT guidoperboli smartphonedataclassificationtechniquefordetectingtheusageofpublicorprivatetransportationmodes AT alessandrorizzo smartphonedataclassificationtechniquefordetectingtheusageofpublicorprivatetransportationmodes |
| _version_ |
1724184325637799936 |