Difftraj: Generating gps trajectory with diffusion probabilistic model
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …
Deep learning for trajectory data management and mining: A survey and beyond
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …
mining, garnering widespread attention due to its crucial role in various practical …
Synmob: Creating high-fidelity synthetic gps trajectory dataset for urban mobility analysis
Urban mobility analysis has been extensively studied in the past decade using a vast
amount of GPS trajectory data, which reveals hidden patterns in movement and human …
amount of GPS trajectory data, which reveals hidden patterns in movement and human …
Social ode: Multi-agent trajectory forecasting with neural ordinary differential equations
Multi-agent trajectory forecasting has recently attracted a lot of attention due to its
widespread applications including autonomous driving. Most previous methods use RNNs …
widespread applications including autonomous driving. Most previous methods use RNNs …
Controltraj: Controllable trajectory generation with topology-constrained diffusion model
Generating trajectory data is among promising solutions to addressing privacy concerns,
collection costs, and proprietary restrictions usually associated with human mobility …
collection costs, and proprietary restrictions usually associated with human mobility …
TrajFormer: Efficient trajectory classification with transformers
Transformers have been an efficient alternative to recurrent neural networks in many
sequential learning tasks. When adapting transformers to modeling trajectories, we …
sequential learning tasks. When adapting transformers to modeling trajectories, we …
Learning to simulate daily activities via modeling dynamic human needs
Daily activity data that records individuals' various types of activities in daily life are widely
used in many applications such as activity scheduling, activity recommendation, and …
used in many applications such as activity scheduling, activity recommendation, and …
Neural ODE differential learning and its application in polar motion prediction
M Kiani Shahvandi, M Schartner… - Journal of Geophysical …, 2022 - Wiley Online Library
This paper introduces a new learning algorithm for accurate, physically driven time series
prediction. The fundamental assumption behind the method is that the phenomena follow …
prediction. The fundamental assumption behind the method is that the phenomena follow …
A deep multimodal network for multi-task trajectory prediction
Addressing the complexity of multi-task trajectory prediction, this study introduces a novel
Deep Multimodal Network (DMN), which integrates a shared feature extractor and a multi …
Deep Multimodal Network (DMN), which integrates a shared feature extractor and a multi …
A survey on diffusion models for time series and spatio-temporal data
The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …