105 citations · 205 across the 27 of their papers we have counts for
9 papers · 1 filter
A Universal Model for Human Mobility Prediction
Qingyue Long, Yuan Yuan, Yong Li
Predicting human mobility is crucial for urban planning, traffic control, and emergency response. Mobility behaviors can be categorized into individual and collective, and these be…
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Haiyang Jiang, Tong Chen, Wentao Zhang +4
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Tem…
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
Yuheng Zhang, Yuan Yuan, Jingtao Ding +2
With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The…
Understanding World or Predicting Future? A Comprehensive Survey of World Models
Jingtao Ding, Yunke Zhang, Yu Shang +12
The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which…
UniFlow: A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction
Yuan Yuan, Jingtao Ding, Chonghua Han +3
Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Tradi…
Diffusion Transformers as Open-World Spatiotemporal Foundation Models
Yuan Yuan, Chonghua Han, Jingtao Ding +3
The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential…
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation
Yuan Yuan, Chenyang Shao, Jingtao Ding +2
Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel g…
UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction
Yuan Yuan, Jingtao Ding, Jie Feng +2
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthrough…
Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs
Chenyang Shao, Fengli Xu, Bingbing Fan +4
The powerful reasoning capabilities of large language models (LLMs) have brought revolutionary changes to many fields, but their performance in human behaviour generation has not y…