ArticlePloS one2020
Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Smartphone Sensor Battery Consumption: A Standardized and Reproducible Test Protocol.Sensors (Basel, Switzerland) · 2026Article
- Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS.Sensors (Basel, Switzerland) · 2024Article
- Investigating the potential of aggregated mobility indices for inferring public transport ridership changes.PloS one · 2024Article
- Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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