Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
A survey on next location prediction techniques, applications, and challenges
AG Chekol, MS Fufa - EURASIP Journal on Wireless Communications and …, 2022 - Springer
Next location prediction has recently gained great attention from researchers due to its
importance in different application areas. Recent growth of location-based service …
importance in different application areas. Recent growth of location-based service …
A utility-aware general framework with quantifiable privacy preservation for destination prediction in LBSs
Destination prediction plays an important role as the basis for a variety of location-based
services (LBSs). However, it poses many threats to users' location privacy. Most related work …
services (LBSs). However, it poses many threats to users' location privacy. Most related work …
TrajVAE: A Variational AutoEncoder model for trajectory generation
Large-scale trajectory dataset is always required for self-driving and many other
applications. In this paper, we focus on the trajectory generation problem, which aims to …
applications. In this paper, we focus on the trajectory generation problem, which aims to …
Attnmove: History enhanced trajectory recovery via attentional network
A considerable amount of mobility data has been accumulated due to the proliferation of
location-based service. Nevertheless, compared with mobility data from transportation …
location-based service. Nevertheless, compared with mobility data from transportation …
Periodicmove: Shift-aware human mobility recovery with graph neural network
Human mobility recovery is of great importance for a wide range of location-based services.
However, recovering human mobility is not trivial because of three challenges: 1) complex …
However, recovering human mobility is not trivial because of three challenges: 1) complex …
[HTML][HTML] LSI-LSTM: An attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points
Individual driving final destination prediction supports location-based services such as
personalized service recommendations, traffic navigation, and public transport dispatching …
personalized service recommendations, traffic navigation, and public transport dispatching …
Predicting destinations by a deep learning based approach
Destination prediction is known as an important problem for many location based services
(LBSs). Existing solutions generally apply probabilistic models or neural network models to …
(LBSs). Existing solutions generally apply probabilistic models or neural network models to …
Online trajectory prediction for metropolitan scale mobility digital twin
Knowing" what is happening" and" what will happen" of the mobility in a city is the building
block of a data-driven smart city system. In recent years, mobility digital twin that makes a …
block of a data-driven smart city system. In recent years, mobility digital twin that makes a …
Spatial transition learning on road networks with deep probabilistic models
In this paper, we study the problem of predicting the most likely traveling route on the road
network between two given locations by considering the real-time traffic. We present a deep …
network between two given locations by considering the real-time traffic. We present a deep …