6 papers
PEMAND: Persona-Enriched Multi-Agent Negotiation for Household Decision-Making
Yuran Sun, Mustafa Sameen, Yaotian Zhang +5
Modeling household-level decisions is central to many real-world applications, including trip planning, residential mobility and migration, disaster management, etc. Existing studi…
Learning Dynamic Belief Graphs for Theory-of-mind Reasoning
Ruxiao Chen, Xilei Zhao, Thomas J. Cova +2
Theory of Mind (ToM) reasoning with Large Language Models (LLMs) requires inferring how people's implicit, evolving beliefs shape what they seek and how they act under uncertainty…
Wildfire Evacuation Analysis Using Facebook Data: Evidence from Palisades and Eaton Fires
Shangkun Jiang, Ruggiero Lovreglio, Thomas J. Cova +3
The growing frequency and intensity of wildfires pose serious threats to communities in wildland-urban interface regions. Understanding evacuation behavior is critical for effectiv…
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Ruxiao Chen, Chenguang Wang, Yuran Sun +2
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate res…
Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learning
Yuran Sun, Susu Xu, Chenguang Wang +1
Big trajectory data hold great promise for human mobility analysis, but their utility is often constrained by the absence of critical traveler attributes, particularly sociodemogra…
Real-time Bus Travel Time Prediction and Reliability Quantification: A Hybrid Markov Model
Yuran Sun, James Spall, Wai Wong +1
Accurate and reliable bus travel time prediction in real-time is essential for improving the operational efficiency of public transportation systems. However, this remains a challe…