Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!
B Deng, D Yang, B Qu, B Fankhauser… - ACM Transactions on …, 2023 - dl.acm.org
As a fundamental problem in human mobility modeling, location prediction forecasts a user's
next location based on historical user mobility trajectories. Recurrent neural networks …
next location based on historical user mobility trajectories. Recurrent neural networks …
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 …
REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories
Location prediction forecasts a user's location based on historical user mobility traces. To
tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts …
tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts …
A Spatial‐Temporal Self‐Attention Network (STSAN) for Location Prediction
S Wang, AL Li, S Xie, WZ Li, BW Wang, S Yao… - …, 2021 - Wiley Online Library
With the popularity of location‐based social networks, location prediction has become an
important task and has gained significant attention in recent years. However, how to use …
important task and has gained significant attention in recent years. However, how to use …
Predicting destinations from partial trajectories using recurrent neural network
Predicting a user's destinations from his or her partial movement trajectories is still a
challenging problem. To this end, we employ recurrent neural networks (RNNs), which can …
challenging problem. To this end, we employ recurrent neural networks (RNNs), which can …
Modeling user activity patterns for next-place prediction
Location has played a very important role in pervasive computing systems. Beyond the
current location, knowing an individual's next location in advance can also enable many …
current location, knowing an individual's next location in advance can also enable many …
Beyond the limits of predictability in human mobility prediction: context-transition predictability
Urban human mobility prediction is forecasting how people move in cities. It is crucial for
many smart city applications including route optimization, preparing for dramatic shifts in …
many smart city applications including route optimization, preparing for dramatic shifts in …
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 …
[图书][B] Recurrent neural network models of human mobility
Z Lin - 2018 - search.proquest.com
Locational data generated by mobile devices present an opportunity to substantially simplify
methodologies and reduce analysis latencies in short-term transportation planning …
methodologies and reduce analysis latencies in short-term transportation planning …
Human mobility prediction with causal and spatial-constrained multi-task network
Modeling human mobility helps to understand how people are accessing resources and
physically contacting with each other in cities, and thus contributes to various applications …
physically contacting with each other in cities, and thus contributes to various applications …