Journal article

Semantics-Aware Hidden Markov Model for Human Mobility

Hongzhi Shi, Yong Li, Hancheng Cao, Xiangxin Zhou, Chao Zhang, Vassilis Kostakos

IEEE Transactions on Knowledge and Data Engineering | IEEE COMPUTER SOC | Published : 2021

Abstract

Understanding human mobility benefits numerous applications such as urban planning, traffic control, and city management. Previous work mainly focuses on modeling spatial and temporal patterns of human mobility. However, the semantics of trajectory are ignored, thus failing to model people's motivation behind mobility. In this paper, we propose a novel semantics-aware mobility model that captures human mobility motivation using large-scale semantic-rich spatial-temporal data from location-based social networks. In our system, we first develop a multimodal embedding method to project user, location, time, and activity on the same embedding space in an unsupervised way while preserving origina..

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Grants

Awarded by National Key Research and Development Program of China


Awarded by National Nature Science Foundation of China


Awarded by Beijing Natural Science Foundation


Awarded by Beijing National Research Center for Information Science and Technology


Funding Acknowledgements

This work was supported in part by The National Key Research and Development Program of China under grant SQ2018YFB180012, the National Nature Science Foundation of China under U1836219, 61971267, 61972223, 61861136003, Beijing Natural Science Foundation under L182038, Beijing National Research Center for Information Science and Technology under 20031887521, and research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology.