TY - GEN
T1 - CoMobile
T2 - 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
AU - Zhang, Desheng
AU - Zhao, Juanjuan
AU - Zhang, Fan
AU - He, Tian
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/11/3
Y1 - 2015/11/3
N2 - Real-time human mobility modeling is essential to various urban applications. To model such human mobility, numerous data-driven techniques have been proposed. However, existing techniques are mostly driven by data from a single view, e.g., a transportation view or a cellphone view, which leads to over-fitting of these single-view models. To address this issue, we propose a human mobility modeling technique based on a generic multi-view learning framework called coMobile. In coMobile, we first improve the performance of single-view models based on tensor decomposition with correlated contexts, and then we integrate these improved single-view models together for multi-view learning to iteratively obtain mutually-reinforced knowledge for real-time human mobility at urban scale. We implement coMobile based on an extremely large dataset in the Chinese city Shenzhen, including data about taxi, bus and subway passengers along with cellphone users, capturing more than 27 thousand vehicles and 10 million urban residents. The evaluation results show that our approach outperforms a single-view model by 51% on average.
AB - Real-time human mobility modeling is essential to various urban applications. To model such human mobility, numerous data-driven techniques have been proposed. However, existing techniques are mostly driven by data from a single view, e.g., a transportation view or a cellphone view, which leads to over-fitting of these single-view models. To address this issue, we propose a human mobility modeling technique based on a generic multi-view learning framework called coMobile. In coMobile, we first improve the performance of single-view models based on tensor decomposition with correlated contexts, and then we integrate these improved single-view models together for multi-view learning to iteratively obtain mutually-reinforced knowledge for real-time human mobility at urban scale. We implement coMobile based on an extremely large dataset in the Chinese city Shenzhen, including data about taxi, bus and subway passengers along with cellphone users, capturing more than 27 thousand vehicles and 10 million urban residents. The evaluation results show that our approach outperforms a single-view model by 51% on average.
KW - Human mobility
KW - Model integration
UR - http://www.scopus.com/inward/record.url?scp=84961211219&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961211219&partnerID=8YFLogxK
U2 - 10.1145/2820783.2820821
DO - 10.1145/2820783.2820821
M3 - Conference contribution
AN - SCOPUS:84961211219
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
A2 - Huang, Yan
A2 - Ali, Mohamed
A2 - Sankaranarayanan, Jagan
A2 - Renz, Matthias
A2 - Gertz, Michael
PB - Association for Computing Machinery
Y2 - 3 November 2015 through 6 November 2015
ER -
