A survey on deep learning for human mobility

M Luca, G Barlacchi, B Lepri… - ACM Computing Surveys …, 2021 - dl.acm.org
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

A survey on trajectory data management, analytics, and learning

S Wang, Z Bao, JS Culpepper, G Cong - ACM Computing Surveys …, 2021 - dl.acm.org
Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …

Deepmove: Predicting human mobility with attentional recurrent networks

J Feng, Y Li, C Zhang, F Sun, F Meng, A Guo… - Proceedings of the 2018 …, 2018 - dl.acm.org
Human mobility prediction is of great importance for a wide spectrum of location-based
applications. However, predicting mobility is not trivial because of three challenges: 1) the …

A survey on trajectory data mining: Techniques and applications

Z Feng, Y Zhu - IEEE Access, 2016 - ieeexplore.ieee.org
Rapid advance of location acquisition technologies boosts the generation of trajectory data,
which track the traces of moving objects. A trajectory is typically represented by a sequence …

Serm: A recurrent model for next location prediction in semantic trajectories

D Yao, C Zhang, J Huang, J Bi - Proceedings of the 2017 ACM on …, 2017 - dl.acm.org
Predicting the next location a user tends to visit is an important task for applications like
location-based advertising, traffic planning, and tour recommendation. We consider the next …

Trajectory clustering via deep representation learning

D Yao, C Zhang, Z Zhu, J Huang… - 2017 international joint …, 2017 - ieeexplore.ieee.org
Trajectory clustering, which aims at discovering groups of similar trajectories, has long been
considered as a corner stone task for revealing movement patterns as well as facilitating …

Gmove: Group-level mobility modeling using geo-tagged social media

C Zhang, K Zhang, Q Yuan, L Zhang… - Proceedings of the …, 2016 - dl.acm.org
Understanding human mobility is of great importance to various applications, such as urban
planning, traffic scheduling, and location prediction. While there has been fruitful research …

Regions, periods, activities: Uncovering urban dynamics via cross-modal representation learning

C Zhang, K Zhang, Q Yuan, H Peng, Y Zheng… - Proceedings of the 26th …, 2017 - dl.acm.org
With the ever-increasing urbanization process, systematically modeling people's activities in
the urban space is being recognized as a crucial socioeconomic task. This task was nearly …

Predicting human mobility via graph convolutional dual-attentive networks

W Dang, H Wang, S Pan, P Zhang, C Zhou… - Proceedings of the …, 2022 - dl.acm.org
Human mobility prediction is of great importance for various applications such as smart
transportation and personalized recommender systems. Although many traditional pattern …

Context-aware deep model for joint mobility and time prediction

Y Chen, C Long, G Cong, C Li - … of the 13th international conference on …, 2020 - dl.acm.org
Mobility prediction, which is to predict where a user will arrive based on the user's historical
mobility records, has attracted much attention. We argue that it is more useful to know not …