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 …
MobilityDL: a review of deep learning from trajectory data
Trajectory data combines the complexities of time series, spatial data, and (sometimes
irrational) movement behavior. As data availability and computing power have increased, so …
irrational) movement behavior. As data availability and computing power have increased, so …
Prompt mining for language-based human mobility forecasting
With the advancement of large language models, language-based forecasting has recently
emerged as an innovative approach for predicting human mobility patterns. The core idea is …
emerged as an innovative approach for predicting human mobility patterns. The core idea is …
Going where, by whom, and at what time: Next location prediction considering user preference and temporal regularity
Next location prediction is a crucial task in human mobility modeling, and is pivotal for many
downstream applications like location-based recommendation and transportation planning …
downstream applications like location-based recommendation and transportation planning …
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 …
[PDF][PDF] Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them.
Trajectory data combines the complexities of time series, spatial data, and (sometimes
irrational) movement behavior. As data availability and computing power have increased, so …
irrational) movement behavior. As data availability and computing power have increased, so …
Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks
Intracytoplasmic sperm injection (ICSI) requires precise selection of a single sperm cell in a
dish to be injected into an oocyte. This task is challenging due to high sperm velocity …
dish to be injected into an oocyte. This task is challenging due to high sperm velocity …
Uncertainty quantification and out-of-distribution detection using surjective normalizing flows
Reliable quantification of epistemic and aleatoric uncertainty is of crucial importance in
applications where models are trained in one environment but applied to multiple different …
applications where models are trained in one environment but applied to multiple different …
Encoding Agent Trajectories as Representations with Sequence Transformers
A Tsiligkaridis, N Kalinowski, Z Li, E Hou - Proceedings of the 7th ACM …, 2024 - dl.acm.org
Spatiotemporal data faces many analogous challenges to natural language text including
the ordering of locations (words) in a sequence, long range dependencies between …
the ordering of locations (words) in a sequence, long range dependencies between …