TY - GEN
T1 - Congestion analysis across locations based on wi-fi signal sensing
AU - Shimada, Atsushi
AU - Oka, Kaito
AU - Igarashi, Masaki
AU - Taniguchi, Rin ichiro
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Many studies related to congestion analysis focus on estimating quantitative values such as actual number of people, mobile devices, and crowd density. In contrast, we focus on perceptual congestion rather than quantitative congestion; however, we also analyze the relationship between quantitative and perceptual congestion. We construct a system for estimating and visualizing congestion and collecting user reports about congestion. We use the number of mobile devices as quantitative congestion measurements obtained from Wi-Fi packet sensors and a user report-based congestion as a perceptual congestion measurement collected via our Web system. In our experiments, we investigate the relationship between these values. In addition, we apply Non-negative Tensor Factorization to extract latent patterns between locations and congestion. These latent features help us to understand the relationship of the characteristics among the locations.
AB - Many studies related to congestion analysis focus on estimating quantitative values such as actual number of people, mobile devices, and crowd density. In contrast, we focus on perceptual congestion rather than quantitative congestion; however, we also analyze the relationship between quantitative and perceptual congestion. We construct a system for estimating and visualizing congestion and collecting user reports about congestion. We use the number of mobile devices as quantitative congestion measurements obtained from Wi-Fi packet sensors and a user report-based congestion as a perceptual congestion measurement collected via our Web system. In our experiments, we investigate the relationship between these values. In addition, we apply Non-negative Tensor Factorization to extract latent patterns between locations and congestion. These latent features help us to understand the relationship of the characteristics among the locations.
UR - http://www.scopus.com/inward/record.url?scp=85048972697&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048972697&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93647-5_12
DO - 10.1007/978-3-319-93647-5_12
M3 - Conference contribution
AN - SCOPUS:85048972697
SN - 9783319936468
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 204
EP - 221
BT - Pattern Recognition Applications and Methods - 6th International Conference, ICPRAM 2017, Revised Selected Papers
A2 - Fred, Ana
A2 - De Marsico, Maria
A2 - di Baja, Gabriella Sanniti
PB - Springer Verlag
T2 - 6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Y2 - 24 February 2017 through 26 February 2017
ER -