TY - JOUR
T1 - Inferring transportation mode from smartphone sensors
T2 - Evaluating the potential of Wi-Fi and Bluetooth
AU - Bjerre-Nielsen, Andreas
AU - Minor, Kelton
AU - Sapieżyński, Piotr
AU - Lehmann, Sune
AU - Lassen, David Dreyer
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0234003
DO - 10.1371/journal.pone.0234003
M3 - Journal article
C2 - 32614842
AN - SCOPUS:85087472390
VL - 15
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 7
M1 - e0234003
ER -