1 citations · 1 across the 6 of their papers we have counts for
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…
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…
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…
Social Vulnerabilities and Wildfire Evacuations: A Case Study of the 2019 Kincade Fire
Yuran Sun, Ana Forrister, Erica D. Kuligowski +3
Vulnerable populations are disproportionately impacted by natural hazards like wildfires. It is crucial to develop equitable and effective evacuation strategies to meet their uniqu…
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
Yuran Sun, Shih-Kai Huang, Xilei Zhao
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane prepared…