105 citations · 125 across the 14 of their papers we have counts for
8 papers · 1 filter
Human Behavior Simulation: Objectives, Methodologies, and Open Problems
Zhang Guozhen, Yu Zihan, Li Nian +4
In recent years, human behavior simulation has drawn increasing attention from both academia and industry. The reasons fall into two aspects. First, simulation serves as a critical…
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…
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper,…
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…
Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread i…
MetroGNN: Metro Network Expansion with Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansi…
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…