Publication

@inproceedings{Chenwei2020_LOIS,

title = {Mobile Sensing of Pedestrian Mobility and its Assessment},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.ieice.org/ken/paper/20200312h1vo/eng/},

isbn = {0913-5685},

year  = {2020},

date = {2020-03-04},

booktitle = {IEICE Tech. Rep.},

volume = {119},

number = {477},

pages = {121--126},

publisher = {IEICE Technical Committee},

organization = {Life Intelligence and Office Information Systems (LOIS)},

abstract = {We propose a client-server system that provides crowd detection and mobility information. Our proposed system has the advantages of low cost and location flexibility without pre-deployed, as long as there is a sufficient number of users involved. We conducted several experiments in real environments to determine the feasibility, accuracy and applicable environment of the system. The result shows that the system can effectively capture the flow of people in the experimental area. In some cases, under the same environment, it can obtain almost the same mobility tracking information from fewer participating users than the GPS method.

},

keywords = {Bluetooth, Crowd detection, Human mobility},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3356995.3364541,

title = {Using Mobile Sensing Technology for Capturing People Mobility Information},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3356995.3364541},

doi = {10.1145/3356995.3364541},

isbn = {9781450369640},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility},

pages = {33–40},

publisher = {Association for Computing Machinery},

address = {Chicago, IL, USA},

series = {PredictGIS’19},

keywords = {Bluetooth, Crowd detection, Human mobility},

pubstate = {published},

tppubtype = {inproceedings}

}