Context-Aware Deep Learning Model for Predicting\Non-Mandatory Activity Locations
C Kuai - 2024 - escholarship.org
The explosion of mobile internet usage has generated vast amounts of data on users'
spatiotemporal activities. This data is crucial for studying human movement, enhancing …
spatiotemporal activities. This data is crucial for studying human movement, enhancing …
DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data
Predicting spatial behaviors of an individual (eg, frequent visits to specific locations) is
important to improve our understanding of the complexity of human mobility patterns, and to …
important to improve our understanding of the complexity of human mobility patterns, and to …
Where to go? Predicting next location in IoT environment
Next location prediction has aroused great interests in the era of internet of things (IoT). With
the ubiquitous deployment of sensor devices, eg, GPS and Wi-Fi, IoT environment offers …
the ubiquitous deployment of sensor devices, eg, GPS and Wi-Fi, IoT environment offers …
[HTML][HTML] ArticlesMSSRM: A Multi-Embedding Based Self-Attention Spatio-temporal Recurrent Model for Human Mobility Prediction
S Wen, X Zhang, R Cao, B Li, Y Li - hcisj.com
Human mobility affects many aspects of anurban 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 …
Predicting human mobility via variational attention
An important task in Location based Social Network applications is to predict mobility-
specifically, user's next point-of-interest (POI)-challenging due to the implicit feedback of …
specifically, user's next point-of-interest (POI)-challenging due to the implicit feedback of …
Time-aware location prediction by convolutional area-of-interest modeling and memory-augmented attentive lstm
Personalized location prediction is key to many mobile applications and services. In this
paper, motivated by both statistical and visualized preliminary analysis on three real …
paper, motivated by both statistical and visualized preliminary analysis on three real …
PGNet: Personalized and Group Preferences Guided Network for Next Place Prediction
H Li, B Wang, F Xia, X Zhai, S Zhu, Y Xu - arXiv preprint arXiv:2110.08266, 2021 - arxiv.org
Predicting the next place to visit is a key in human mobility behavior modeling, which plays a
significant role in various fields, such as epidemic control, urban planning, traffic …
significant role in various fields, such as epidemic control, urban planning, traffic …
Predicting human mobility with reinforcement-learning-based long-term periodicity modeling
Mobility prediction plays an important role in a wide range of location-based applications
and services. However, there are three problems in the existing literature:(1) explicit high …
and services. However, there are three problems in the existing literature:(1) explicit high …
[HTML][HTML] Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data
J Li, W Guo, H Liu, X Chen, A Yu, J Li - ISPRS International Journal of Geo …, 2021 - mdpi.com
Predicting user activity intensity is crucial for various applications. However, existing studies
have two main problems. First, as user activity intensity is nonstationary and nonlinear …
have two main problems. First, as user activity intensity is nonstationary and nonlinear …
Revealing behavioral impact on mobility prediction networks through causal interventions
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate
internal workings pose challenges for interpretability, especially in comprehending how …
internal workings pose challenges for interpretability, especially in comprehending how …