4 citations · 4 across the 6 of their papers we have counts for
6 papers
Neural-Bayesian Structure Learning for Discrete Choice Modeling
Hyunsoo Yun, Eun Hak Lee, Jiaru Zhang +2
Conventional discrete choice and machine learning models are estimated primarily from observational data and typically treat explanatory covariates as parallel inputs, providing no…
Knowledge-Data-Dual-Driven Reinforcement Learning for Autonomous Vehicle Control in Mixed Traffic
Jie Fang, Wei Zheng, Mengyun Xu +1
In mixed traffic, decision-making for autonomous vehicles (AVs) confronts three interrelated challenges. First, physics-based priors incorporated into reinforcement learning (RL) m…
Physics Informed Multi-task Joint Generative Learning for Arterial Vehicle Trajectory Reconstruction Considering Lane Changing Behavior
Mengyun Xu, Jie Fang, Eui-Jin Kim +2
Reconstructing complete traffic flow time-space diagrams from vehicle trajectories offer a comprehensive view on traffic dynamics at arterial intersections. However, obtaining full…
A Large Language Model for Feasible and Diverse Population Synthesis
Sung Yoo Lim, Hyunsoo Yun, Prateek Bansal +2
Generating a synthetic population that is both feasible and diverse is crucial for ensuring the validity of downstream activity schedule simulation in activity-based models (ABMs).…
Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making
David Chen, Xinwei Li, Eui-Jin Kim +2
Statistical models are often defined by a generative process for simulating synthetic data, but this can lead to intractable likelihoods. Likelihood free inference (LFI) methods en…
A Deep Generative Model for Feasible and Diverse Population Synthesis
Eui-Jin Kim, Prateek Bansal
An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual- and household-level attributes in the actual population. Since the e…