TY - GEN
T1 - Activity Characterization for Modeling Behavioral-driven Human Mobility in Platial Networks
AU - Thakur, Gautam
AU - Kotevska, Olivera
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/11/3
Y1 - 2020/11/3
N2 - The population is increasingly becoming tractable as more and more people carry handheld devices as part of their everyday activities. Recent studies have shown that handheld devices' generated traffic share is now more than 50% of total global online traffic. This has created an unprecedented opportunity for modeling human mobility behavior. For example, aggregate check-ins and dwell time can reveal building level occupancies. However, there are clear limits to accurate modeling (e.g. reproducible, repeatable, and realistic), unless we decipher the underlying reason causing typical mobility patterns. We know that human behavior is a reflection of a set of activities, such as going to the gym or work, and which can be seen as a catalyst for humans to move from one location to another. This work envisions the use of activity characterization for modeling human mobility by introducing a context that maps activities to certain mobility patterns. In the end, we highlight the efficacy of the proposed approach by analyzing the impact of public policies surrounding stay at home order on human mobility.
AB - The population is increasingly becoming tractable as more and more people carry handheld devices as part of their everyday activities. Recent studies have shown that handheld devices' generated traffic share is now more than 50% of total global online traffic. This has created an unprecedented opportunity for modeling human mobility behavior. For example, aggregate check-ins and dwell time can reveal building level occupancies. However, there are clear limits to accurate modeling (e.g. reproducible, repeatable, and realistic), unless we decipher the underlying reason causing typical mobility patterns. We know that human behavior is a reflection of a set of activities, such as going to the gym or work, and which can be seen as a catalyst for humans to move from one location to another. This work envisions the use of activity characterization for modeling human mobility by introducing a context that maps activities to certain mobility patterns. In the end, we highlight the efficacy of the proposed approach by analyzing the impact of public policies surrounding stay at home order on human mobility.
KW - mobility
KW - modeling
KW - pattern recognition
KW - probability and statistics
UR - http://www.scopus.com/inward/record.url?scp=85096320307&partnerID=8YFLogxK
U2 - 10.1145/3423334.3431449
DO - 10.1145/3423334.3431449
M3 - Conference contribution
AN - SCOPUS:85096320307
T3 - ACM International Conference Proceeding Series
BT - LocalRec 2020 - Proceedings of the 4th ACM SIGSPATIAL International Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising
A2 - Bouros, Panagiotis
A2 - Dasu, Tamraparni
A2 - Kanza, Yaron
A2 - Renz, Matthias
A2 - Sacharidis, Dimitris
PB - Association for Computing Machinery
T2 - 4th ACM SIGSPATIAL International Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising, LocalRec 2020, 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2020
Y2 - 3 November 2020
ER -