3 papers
cs.LG2026
RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
Yuhan Tang, Kangxin Cui, Jung Ho Park +6
Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matc…
cs.AI2025
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning
Yuebing Liang, Shenhao Wang, Jiangbo Yu +3
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To a…
cs.HC2025
Modular Conversational Agents for Surveys and Interviews
Jiangbo Yu, Jinhua Zhao, Luis Miranda-Moreno +1
Surveys and interviews are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalabili…