@article{oai:ipsj.ixsq.nii.ac.jp:00193887,
 author = {Thongtat, Oransirikul and Ian, Piumarta and Hideyuki, Takada and Thongtat, Oransirikul and Ian, Piumarta and Hideyuki, Takada},
 issue = {1},
 journal = {情報処理学会論文誌},
 month = {Jan},
 note = {Quality of service is one factor passengers consider when deciding to use the public transportation system. Increasing the quality of service can be done in several ways, such as improving on-time arrival, reducing waiting time, and providing seat availability information. We believe that if passengers have better information for making decisions, that can increase the quality of service. This paper proposes estimating the number of passengers by analyzing signals from their Wi-Fi devices, classifying them as originating from passenger or non-passenger devices using a real-time filtering mechanism. Experimental validation was performed aboard busses of different types taking different routes. Our experimental results show that filtered data can classify passenger device signals from environmental ones with an accuracy of 75 percent, which is a promising basis for providing real-time information to passengers that improves the quality of their service. 
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.27(2019) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.27.25
------------------------------, Quality of service is one factor passengers consider when deciding to use the public transportation system. Increasing the quality of service can be done in several ways, such as improving on-time arrival, reducing waiting time, and providing seat availability information. We believe that if passengers have better information for making decisions, that can increase the quality of service. This paper proposes estimating the number of passengers by analyzing signals from their Wi-Fi devices, classifying them as originating from passenger or non-passenger devices using a real-time filtering mechanism. Experimental validation was performed aboard busses of different types taking different routes. Our experimental results show that filtered data can classify passenger device signals from environmental ones with an accuracy of 75 percent, which is a promising basis for providing real-time information to passengers that improves the quality of their service. 
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.27(2019) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.27.25
------------------------------},
 title = {Classifying Passenger and Non-passenger Signals in Public Transportation by Analysing Mobile Device Wi-Fi Activity},
 volume = {60},
 year = {2019}
}