Self-supervised human mobility learning for next location prediction and trajectory classification
Massive digital mobility data are accumulated nowadays due to the proliferation of location-
based service (LBS), which provides the opportunity of learning knowledge from human …
based service (LBS), which provides the opportunity of learning knowledge from human …
Contextual spatio-temporal graph representation learning for reinforced human mobility mining
The rapid development of location-based services spurred a large number of user-centric
applications. Particularly, an interesting topic has attracted the attention of researchers that …
applications. Particularly, an interesting topic has attracted the attention of researchers that …
Adversarial mobility learning for human trajectory classification
Understanding human mobility is one of the important but challenging tasks in Location-
based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User …
based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User …
Geo-aware graph-augmented self-attention network for individual mobility prediction
Y Wang, H Chen, S Liu, K Wang, Y Hu - Future Generation Computer …, 2024 - Elsevier
Even though some studies have found encouraging results, the sparsity of data and the
complexity of mobility patterns remain significant challenges in predicting individual mobility …
complexity of mobility patterns remain significant challenges in predicting individual mobility …
Predicting collective human mobility via countering spatiotemporal heterogeneity
Human mobility forecasting is the key to energizing considerable mobile computing
services. However, we find that the collective mobility suffers the spatiotemporal …
services. However, we find that the collective mobility suffers the spatiotemporal …
Semantics-aware hidden Markov model for human mobility
Understanding human mobility benefits numerous applications such as urban planning,
traffic control, and city management. Previous work mainly focuses on modeling spatial and …
traffic control, and city management. Previous work mainly focuses on modeling spatial and …
[PDF][PDF] Identifying Human Mobility via Trajectory Embeddings.
Understanding human trajectory patterns is an important task in many location based social
networks (LBSNs) applications, such as personalized recommendation and preference …
networks (LBSNs) applications, such as personalized recommendation and preference …
[PDF][PDF] MSSRM: A multi-embedding based self-attention spatio-temporal recurrent model for human mobility prediction
S Wen, X Zhang, R Cao, B Li, Y Li - HCIS, 2021 - hcisj.com
Human mobility affects many aspects of an urban area, including spatial structure, temporal
connectivity, even response to epidemics. Prediction of human mobility is of great …
connectivity, even response to epidemics. Prediction of human mobility is of great …
Location prediction over sparse user mobility traces using rnns
Location prediction is a key problem in human mobility modeling, which predicts a user's
next location based on historical user mobility traces. As a sequential prediction problem by …
next location based on historical user mobility traces. As a sequential prediction problem by …
[PDF][PDF] Deep learning for human mobility: a survey on data and models
Urban population is increasing strikingly and human mobility is becoming more complex
and bulky, affecting crucial aspects of people lives such as the spreading of viral diseases …
and bulky, affecting crucial aspects of people lives such as the spreading of viral diseases …