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Found 26 papers, 14 papers with code
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
To address these challenges, we propose TransferTraj, a vehicle GPS trajectory learning model that excels in both region and task transferability.
T2S: High-resolution Time Series Generation with Text-to-Series Diffusion Models
Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series datasets across domains.
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation.
Vision-Enhanced Time Series Forecasting via Latent Diffusion Models
In this paper, we propose LDM4TS, a novel framework that leverages the powerful image reconstruction capabilities of latent diffusion models for vision-enhanced time series forecasting.
Embracing Large Language Models in Traffic Flow Forecasting
Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network.
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services
We also present a representative implementation of DRL4AOI - TrajRL4AOI - for AOI segmentation in the logistics service.
Uncertainty-aware Human Mobility Modeling and Anomaly Detection
Notably, we equip our proposed model USTAD (for Uncertainty-aware Spatio-Temporal Anomaly Detection) with aleatoric (i. e. data) uncertainty estimation to account for inherent stochasticity in certain individuals' behavior, as well as epistemic (i. e. model) uncertainty to handle data sparsity under a large variety of human behaviors.
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!
Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few.
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
This involves two main challenges: 1) Predicting a path that aligns with the ground truth, and 2) modeling the impact of travel time in each segment on overall uncertainty under varying conditions.
PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba
To better utilize vehicle trajectories, it is essential to develop a trajectory learning approach that can effectively and efficiently extract rich semantic information, including movement behavior and travel purposes, to support accurate downstream applications.
TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability
A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus improving computational efficiency and effectiveness with limited training data.
UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings
Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings.
Learning Geospatial Region Embedding with Heterogeneous Graph
In this paper, we present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks.
TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories
Second, TrajCogn introduces a new trajectory prompt that integrates these patterns and purposes into LLMs, allowing the model to adapt to various tasks.
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.
Foundation Models for Time Series Analysis: A Tutorial and Survey
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models
In this technical report, we explore the integration of LLMs and the popular academic writing tool, Overleaf, to enhance the efficiency and quality of academic writing.
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
In this paper, we introduce for the first time the concept of spatio-temporal data dynamic sparse training and are committed to adaptively, dynamically filtering important sensor distributions.
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).
UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web
To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP).
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System
How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system.
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting.
